Systems and methods for automated and direct  network positioning

ABSTRACT

Systems and methods for automated and direct network positioning are disclosed herein. The system can include memory that can include a content library database and a structure sub-database. The system can include at least one server. The at least one server can: receive a data packet previously unassociated with the hierarchy in the structure sub-database; identify one of the plurality of positions within the hierarchy for the data packet; receive user information; present a series of assessment data packets to the user; receive a response from the user subsequent to presentation of each of the assessment data packets; evaluate the received responses; adjust a location of the user within the hierarchy based on the evaluating of the received responses; and present a content data packet to the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/651,004, filed on Mar. 30, 2018, and entitled “AUTOGENERATION OF MATHEXERCISES WITH STEP-LEVEL EXPRESSION TREES AND LEARNING OBJECTIVESTAGGED FOR USE WITH MOBILE OCR TECHNOLOGIES IN INNER LOOP ADAPTIVELEARNING MODELS”, the entirety of which is hereby incorporated byreference herein.

BACKGROUND

A computer network or data network is a telecommunications network whichallows computers to exchange data. In computer networks, networkedcomputing devices exchange data with each other along network links(data connections). The connections between nodes are established usingeither cable media or wireless media. The best-known computer network isthe Internet.

Network computer devices that originate, route, and terminate the dataare called network nodes. Nodes can include hosts such as personalcomputers, phones, servers, as well as networking hardware. Two suchdevices can be said to be networked together when one device is able toexchange information with the other device, whether or not they have adirect connection to each other.

Computer networks differ in the transmission media used to carry theirsignals, the communications protocols to organize network traffic, thenetwork's size, topology and organizational intent. In most cases,communications protocols are layered on (i.e. work using) other morespecific or more general communications protocols, except for thephysical layer that directly deals with the transmission media.

BRIEF SUMMARY

One aspect of the present disclosure relates to a system for automatedand direct network positioning. The system includes a memory including acontent library database containing: a plurality of assessment datapackets and a plurality of content data packets; and a structuresub-database identifying a hierarchy with which each of the plurality ofassessment data packets and each of the content data packets areassociated. In some embodiments, the hierarchy includes a plurality ofpositions. The system can include at least one server. The at least oneserver can: receive a data packet previously unassociated with thehierarchy in the structure sub-database; identify one of the pluralityof positions within the hierarchy for the data packet; receive userinformation; present a series of assessment data packets to the user;receive a response from the user subsequent to presentation of each ofthe assessment data packets; evaluate the received responses; adjust alocation of the user within the hierarchy based on the evaluating of thereceived responses; and present a content data packet to the user. Insome embodiments, the content data packet is selected for presenting tothe user based on the adjusted location of the user within thehierarchy.

In some embodiments, the series of assessment data packets can includethe received data packet. In some embodiments, the presented contentdata packet can include the received data packet. In some embodiments,the location of the user within the hierarchy is adjusted according to amachine-learning model. In some embodiments, each of the assessment datapackets is unassociated with an independent difficulty level.

In some embodiments, the difficulty level of each of the assessment datapackets is represented by the position of each of the assessment datapackets in the hierarchy. In some embodiments, presenting a series ofassessment data packets includes: determining an instantaneous userlocation in the hierarchy; selecting an assessment data packet based onthe instantaneous user location in the hierarchy; and presenting theselected assessment data packet to the user. In some embodiments,evaluating user responses includes: determine correctness of receivedresponse; determining an adjustment to the instantaneous user locationin the hierarchy based on the correctness of the received response; andupdating the instantaneous user location in the hierarchy according tothe adjustment.

In some embodiments, determining an adjustment to the instantaneous userlocation in the hierarchy includes: determining for a plurality ofpositions within the hierarchy a probability of the user correctlyresponding to assessment data packets of each of those plurality ofpositions within the hierarchy according to a logistic model; comparingthe probabilities of the user correctly responding to the assessmentdata packets with a mastery threshold; and identifying the lowestposition within the hierarchy for which the probability of the usercorrectly responding to the assessment data packets of that positionfails to meet or exceed the mastery threshold. In some embodiments, thelogistic model comprises a three parameter logistic model. In someembodiments, the three parameter logistic model determines a probabilityof the user correctly responding to assessment data packets of aposition within the hierarchy according to the user's instantaneous userlocation in the hierarchy.

In some embodiments, the user's instantaneous user location in thehierarchy is determined according to a estimation method. In someembodiments, the estimation method includes an expected a posteriori(EAP) estimation method. In some embodiments, the assessment data packetis selected in part based on information criterion values, whichinformation criterion can be Fisher information, calculated forassessment data packets available for selecting. In some embodiments,one of the assessment data packets is available for selecting when theone of the assessment data packets is not previously presented to theuser. In some embodiments, selecting the assessment data packet based oninformation criterion values includes: identifying assessment datapackets available for selecting; calculating a information criterionvalue for each of the of the assessment data packets available forselecting; calculating an average information criterion value for eachposition in the hierarchy from the information criterion values of theassessment data packets associated with each position in the hierarchy;identifying the position in the hierarchy having the highest averageinformation criterion value; and selecting one of the assessment datapackets associated with the position in the hierarchy identified ashaving the highest average information criterion value. In someembodiments, the one of the assessment data packets is randomly selectedfrom the assessment data packets of the position in the hierarchyidentified as having the highest average information criterion value.

One aspect of the present disclosure relates to a method for automatedand direct network positioning. The method includes: receiving userinformation from a user; presenting a series of assessment data packetsto the user; receiving a response from the user subsequent topresentation of each of the assessment data packets; evaluating thereceived responses; adjusting a location of the user within thehierarchy based on the evaluating of the received responses; andpresenting a content data packet to the user. In some embodiments, thecontent data packet is selected for presenting to the user based on theadjusted location of the user within the hierarchy.

In some embodiments, the method includes: receiving a data packetpreviously unassociated with the hierarchy in the structuresub-database; and identifying one of the plurality of positions withinthe hierarchy for the data packet; and associating the received datapacket with the one of the positions within the hierarchy. In someembodiments, the series of assessment data packets includes the receiveddata packet, and/or the presented content data packet includes thereceived data packet. In some embodiments, presenting a series ofassessment data packets includes: determining an instantaneous userlocation in the hierarchy; selecting an assessment data packet based onthe instantaneous user location in the hierarchy; and presenting theselected assessment data packet to the user. In some embodiments,evaluating user responses includes: evaluating the received response;determining an adjustment to the instantaneous user location in thehierarchy; and updating the instantaneous user location in the hierarchyaccording to the adjustment.

One aspect of the present disclosure relates to a system for automateduser interface customization. The system includes memory containing acontent library database including a plurality of assessment datapackets and a plurality of content data packets. The system includes atleast one server. The at least one server can: receive user information,which user information identifies a user; direct launch of a userinterface on a user device, the user interface including a plurality ofwindows in which content is presented to the user and in which a userresponse is received; present assessment data packets to the user viathe user interface; generate a readiness value based on user responsesreceived to the presented assessment data packets; progressivelydeactivate functionalities of the user interface as the readiness valueincreases; and trigger delivery of a mastery assessment when thereadiness value exceeds a threshold.

In some embodiments, the at least one server can: retrieve a masterythreshold; and compare the readiness value to the mastery threshold. Insome embodiments, generating the readiness value includes: identifying aset of responses received from the user; determining usage of masteryfacilitators associated with the identified set of responses receivedfrom the user; and generating a score for the identified set ofresponses. In some embodiments, the functionalities of the first userinterface include content presentation functionalities. In someembodiments, the content presentation functionalities can includemastery facilitators.

In some embodiments, the at least one server can further retrieve aplurality of deactivation thresholds, each of which plurality ofdeactivation thresholds is associated with a readiness value andfunctionality for deactivation when the deactivation threshold is met orexceeded. In some embodiments, the deactivation of functionalities ofthe user interface include modifying the user interface.

In some embodiments, the at least one server can further select andpresent content to the user until a termination criterion is met. Insome embodiments, the termination criterion includes a masterythreshold. In some embodiments, the set of responses can include fourresponses.

One aspect of the present disclosure relates to a method for automateduser interface customization. The method includes: receiving userinformation from a user device at least one server, which userinformation identifies a user; directing with the at least one serverlaunch of a user interface on the user device, the user interfaceincluding a plurality of windows in which content is presented to theuser and in which a user response is received; presenting assessmentdata packets to the user via the user interface; generating a readinessvalue based on user responses received to the presented assessment datapackets; progressively deactivating functionalities of the userinterface as the readiness value increases; and triggering delivery of amastery assessment when the readiness value exceeds a threshold.

In some embodiments, the method includes: retrieving a masterythreshold; and comparing the readiness value to the mastery threshold.In some embodiments, generating the readiness value includes:identifying a set of responses received from the user; determining usageof mastery facilitators associated with the identified set of responsesreceived from the user; and generating a score for the identified set ofresponses. In some embodiments, the functionalities of the first userinterface include content presentation functionalities. In someembodiments, the content presentation functionalities comprise masteryfacilitators.

In some embodiments, the method can include retrieving a plurality ofdeactivation thresholds. In some embodiments, each of the plurality ofdeactivation thresholds is associated with a readiness value andfunctionality for deactivation when the deactivation threshold is met orexceeded. In some embodiments, the deactivation of functionalities ofthe user interface includes modifying the user interface.

In some embodiments, the method includes iteratively selecting andpresenting content to the user until a termination criterion is met. Insome embodiments, the termination criterion can be a mastery threshold.In some embodiments, the set of responses comprises four responses.

Further areas of applicability of the present disclosure will becomeapparent from the detailed description provided hereinafter. It shouldbe understood that the detailed description and specific examples, whileindicating various embodiments, are intended for purposes ofillustration only and are not intended to necessarily limit the scope ofthe disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example of a contentdistribution network.

FIG. 2 is a block diagram illustrating a computer server and computingenvironment within a content distribution network.

FIG. 3 is a block diagram illustrating an embodiment of one or more datastore servers within a content distribution network.

FIG. 4 is a block diagram illustrating an embodiment of one or morecontent management servers within a content distribution network.

FIG. 5 is a block diagram illustrating the physical and logicalcomponents of a special-purpose computer device within a contentdistribution network.

FIG. 6 is a block diagram illustrating one embodiment of thecommunication network.

FIG. 7 is a block diagram illustrating one embodiment of user device andsupervisor device communication.

FIG. 8 is a schematic illustration of one embodiment of a computingstack.

FIG. 9 is a schematic illustration of one embodiment of communicationand processing flow of modules within the content distribution network.

FIG. 10 is a schematic illustration of one embodiment of communicationand processing flow of modules within the content distribution network.

FIG. 11 is a schematic illustration of one embodiment of communicationand processing flow of modules within the content distribution network.

FIG. 12 is a schematic illustration of one embodiment of communicationand processing flow of modules within the content distribution network.

FIG. 13 is a flowchart illustrating one embodiment of a process for datamanagement.

FIG. 14 is a flowchart illustrating one embodiment of a process forevaluating a response.

FIG. 15 is a flowchart illustrating one embodiment of a process forautomated determining of user position within the hierarchy.

FIG. 16 is a flowchart illustrating one embodiment of a process for datapacket presentation.

FIG. 17 is a flowchart illustrating one embodiment of a process forevaluating user responses.

FIG. 18 is a flowchart illustrating one embodiment of a process fordetermining the adjustment to the instantaneous user location.

FIG. 19 is a flowchart illustrating one embodiment of a process forselecting an assessment data packet.

FIG. 20 is a schematic illustration of one embodiment of an exemplaryuser interface (UX).

FIG. 21 is flowchart illustrating one embodiment of a process forautomated user interface customization.

FIG. 22 is a flowchart illustrating one embodiment of a process 870 forgenerating a readiness score.

In the appended figures, similar components and/or features may have thesame reference label. Further, various components of the same type maybe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

The ensuing description provides illustrative embodiment(s) only and isnot intended to limit the scope, applicability, or configuration of thedisclosure. Rather, the ensuing description of the illustrativeembodiment(s) will provide those skilled in the art with an enablingdescription for implementing a preferred exemplary embodiment. It isunderstood that various changes can be made in the function andarrangement of elements without departing from the spirit and scope asset forth in the appended claims.

Current machine learning models for grading are large and cumbersomemodels. These are trained with large sets of training data and are notcustomized to the tendencies of a single specific grader. Due to thereliance of these models on large sets of training data, these modelscan be used in circumstances with smaller set of data. Specifically, asthe size of the set of training data decreases, the accuracy of themodel diminishes.

While these models are, in some aspects, satisfactory for grading largenumbers of responses to the same question or prompt, they can beunsatisfactory in other circumstances. Limitations of these models areparticularly apparent in their inability to be used in grading and/orevaluating small numbers of responses to unique questions and/oraccording to unique or customized criteria. Thus, while gradingtechnology has improved for large-scale testing, grading for small-scaletesting still relies on human graders.

The present disclosure relates to systems and methods for providingcustomizable machine learning grading. This can include the customizingof a model according to one or several attributes of the teacher and/orthe teachers grading preference. In some embodiments, this can includethe generating and/or customizing of one or several models for gradingcustom prompts. The training and/or customization of the models caninclude identification and use of pre-existing data to perform a portionof the training. The use of the pre-existing data can effectivelyincrease the size of the set of training data. In some embodiments,training can be further accelerated by the identification of one orseveral responses for manual grading, which one or several responses canbe identified as representative of some or all of the receivedresponses. Due to the representativeness of these identified one orseveral responses, their manual grading and inclusion in the trainingset can accelerate the completion training.

The training and customization of the models can include iterativeretraining of the model and/or iterative generation of new piece oftraining data based on inputs received from a user such as thecustomizer the model. In some embodiments, for example, after the modelhas been trained, evaluation output of the model can be provided to theuser. The user can provide feedback, which can include acceptance of theresults indicated in the evaluation output and/or a request for furthertraining of the model.

Systems and methods according to the disclosure herein acceleratetraining of machine learning models and improve performance of machinelearning models trained with small data sets. Further, systems andmethods according to the disclosure herein provide for automated gradingof custom and/or customized prompts

With reference now to FIG. 1, a block diagram is shown illustratingvarious components of a content distribution network (CDN) 100 whichimplements and supports certain embodiments and features describedherein. In some embodiments, the content distribution network 100 cancomprise one or several physical components and/or one or severalvirtual components such as, for example, one or several cloud computingcomponents. In some embodiments, the content distribution network 100can comprise a mixture of physical and cloud computing components.

Content distribution network 100 may include one or more contentmanagement servers 102. As discussed below in more detail, contentmanagement servers 102 may be any desired type of server including, forexample, a rack server, a tower server, a miniature server, a bladeserver, a mini rack server, a mobile server, an ultra-dense server, asuper server, or the like, and may include various hardware components,for example, a motherboard, a processing unit, memory systems, harddrives, network interfaces, power supplies, etc. Content managementserver 102 may include one or more server farms, clusters, or any otherappropriate arrangement and/or combination or computer servers. Contentmanagement server 102 may act according to stored instructions locatedin a memory subsystem of the server 102, and may run an operatingsystem, including any commercially available server operating systemand/or any other operating systems discussed herein.

The content distribution network 100 may include one or more data storeservers 104, such as database servers and file-based storage systems.The database servers 104 can access data that can be stored on a varietyof hardware components. These hardware components can include, forexample, components forming tier 0 storage, components forming tier 1storage, components forming tier 2 storage, and/or any other tier ofstorage. In some embodiments, tier 0 storage refers to storage that isthe fastest tier of storage in the database server 104, andparticularly, the tier 0 storage is the fastest storage that is not RAMor cache memory. In some embodiments, the tier 0 memory can be embodiedin solid state memory such as, for example, a solid-state drive (SSD)and/or flash memory.

In some embodiments, the tier 1 storage refers to storage that is one orseveral higher performing systems in the memory management system, andthat is relatively slower than tier 0 memory, and relatively faster thanother tiers of memory. The tier 1 memory can be one or several harddisks that can be, for example, high-performance hard disks. These harddisks can be one or both of physically or communicatively connected suchas, for example, by one or several fiber channels. In some embodiments,the one or several disks can be arranged into a disk storage system, andspecifically can be arranged into an enterprise class disk storagesystem. The disk storage system can include any desired level ofredundancy to protect data stored therein, and in one embodiment, thedisk storage system can be made with grid architecture that createsparallelism for uniform allocation of system resources and balanced datadistribution.

In some embodiments, the tier 2 storage refers to storage that includesone or several relatively lower performing systems in the memorymanagement system, as compared to the tier 1 and tier 2 storages. Thus,tier 2 memory is relatively slower than tier 1 and tier 0 memories. Tier2 memory can include one or several SATA-drives (e.g., Serial ATAttachment drives) or one or several NL-SATA drives.

In some embodiments, the one or several hardware and/or softwarecomponents of the database server 104 can be arranged into one orseveral storage area networks (SAN), which one or several storage areanetworks can be one or several dedicated networks that provide access todata storage, and particularly that provides access to consolidated,block level data storage. A SAN typically has its own network of storagedevices that are generally not accessible through the local area network(LAN) by other devices. The SAN allows access to these devices in amanner such that these devices appear to be locally attached to the userdevice.

Data stores 104 may comprise stored data relevant to the functions ofthe content distribution network 100. Illustrative examples of datastores 104 that may be maintained in certain embodiments of the contentdistribution network 100 are described below in reference to FIG. 3. Insome embodiments, multiple data stores may reside on a single server104, either using the same storage components of server 104 or usingdifferent physical storage components to assure data security andintegrity between data stores. In other embodiments, each data store mayhave a separate dedicated data store server 104.

Content distribution network 100 also may include one or more userdevices 106 and/or supervisor devices 110. User devices 106 andsupervisor devices 110 may display content received via the contentdistribution network 100, and may support various types of userinteractions with the content. User devices 106 and supervisor devices110 may include mobile devices such as smartphones, tablet computers,personal digital assistants, and wearable computing devices. Such mobiledevices may run a variety of mobile operating systems and may be enabledfor Internet, e-mail, short message service (SMS), Bluetooth®, mobileradio-frequency identification (M-RFID), and/or other communicationprotocols. Other user devices 106 and supervisor devices 110 may begeneral purpose personal computers or special-purpose computing devicesincluding, by way of example, personal computers, laptop computers,workstation computers, projection devices, and interactive room displaysystems. Additionally, user devices 106 and supervisor devices 110 maybe any other electronic devices, such as a thin-client computers, anInternet-enabled gaming systems, business or home appliances, and/or apersonal messaging devices, capable of communicating over network(s)120.

In different contexts of content distribution networks 100, user devices106 and supervisor devices 110 may correspond to different types ofspecialized devices, for example, student devices and teacher devices inan educational network, employee devices and presentation devices in acompany network, different gaming devices in a gaming network, etc. Insome embodiments, user devices 106 and supervisor devices 110 mayoperate in the same physical location 107, such as a classroom orconference room. In such cases, the devices may contain components thatsupport direct communications with other nearby devices, such aswireless transceivers and wireless communications interfaces, Ethernetsockets or other Local Area Network (LAN) interfaces, etc. In otherimplementations, the user devices 106 and supervisor devices 110 neednot be used at the same location 107, but may be used in remotegeographic locations in which each user device 106 and supervisor device110 may use security features and/or specialized hardware (e.g.,hardware-accelerated SSL and HTTPS, WS-Security, firewalls, etc.) tocommunicate with the content management server 102 and/or other remotelylocated user devices 106. Additionally, different user devices 106 andsupervisor devices 110 may be assigned different designated roles, suchas presenter devices, teacher devices, administrator devices, or thelike, and in such cases the different devices may be provided withadditional hardware and/or software components to provide content andsupport user capabilities not available to the other devices.

The content distribution network 100 also may include a privacy server108 that maintains private user information at the privacy server 108while using applications or services hosted on other servers. Forexample, the privacy server 108 may be used to maintain private data ofa user within one jurisdiction even though the user is accessing anapplication hosted on a server (e.g., the content management server 102)located outside the jurisdiction. In such cases, the privacy server 108may intercept communications between a user device 106 or supervisordevice 110 and other devices that include private user information. Theprivacy server 108 may create a token or identifier that does notdisclose the private information and may use the token or identifierwhen communicating with the other servers and systems, instead of usingthe user's private information.

As illustrated in FIG. 1, the content management server 102 may be incommunication with one or more additional servers, such as a contentserver 112, a user data server 114, and/or an administrator server 116.Each of these servers may include some or all of the same physical andlogical components as the content management server(s) 102, and in somecases, the hardware and software components of these servers 112-116 maybe incorporated into the content management server(s) 102, rather thanbeing implemented as separate computer servers.

Content server 112 may include hardware and software components togenerate, store, and maintain the content resources for distribution touser devices 106 and other devices in the network 100. For example, incontent distribution networks 100 used for professional training andeducational purposes, content server 112 may include data stores oftraining materials, presentations, plans, syllabi, reviews, evaluations,interactive programs and simulations, course models, course outlines,and various training interfaces that correspond to different materialsand/or different types of user devices 106. In content distributionnetworks 100 used for media distribution, interactive gaming, and thelike, a content server 112 may include media content files such asmusic, movies, television programming, games, and advertisements.

User data server 114 may include hardware and software components thatstore and process data for multiple users relating to each user'sactivities and usage of the content distribution network 100. Forexample, the content management server 102 may record and track eachuser's system usage, including their user device 106, content resourcesaccessed, and interactions with other user devices 106. This data may bestored and processed by the user data server 114, to support usertracking and analysis features. For instance, in the professionaltraining and educational contexts, the user data server 114 may storeand analyze each user's training materials viewed, presentationsattended, courses completed, interactions, evaluation results, and thelike. The user data server 114 may also include a repository foruser-generated material, such as evaluations and tests completed byusers, and documents and assignments prepared by users. In the contextof media distribution and interactive gaming, the user data server 114may store and process resource access data for multiple users (e.g.,content titles accessed, access times, data usage amounts, gaminghistories, user devices and device types, etc.).

Administrator server 116 may include hardware and software components toinitiate various administrative functions at the content managementserver 102 and other components within the content distribution network100. For example, the administrator server 116 may monitor device statusand performance for the various servers, data stores, and/or userdevices 106 in the content distribution network 100. When necessary, theadministrator server 116 may add or remove devices from the network 100,and perform device maintenance such as providing software updates to thedevices in the network 100. Various administrative tools on theadministrator server 116 may allow authorized users to set user accesspermissions to various content resources, monitor resource usage byusers and devices 106, and perform analyses and generate reports onspecific network users and/or devices (e.g., resource usage trackingreports, training evaluations, etc.).

The content distribution network 100 may include one or morecommunication networks 120. Although only a single network 120 isidentified in FIG. 1, the content distribution network 100 may includeany number of different communication networks between any of thecomputer servers and devices shown in FIG. 1 and/or other devicesdescribed herein. Communication networks 120 may enable communicationbetween the various computing devices, servers, and other components ofthe content distribution network 100. As discussed below, variousimplementations of content distribution networks 100 may employdifferent types of networks 120, for example, computer networks,telecommunications networks, wireless networks, and/or any combinationof these and/or other networks.

The content distribution network 100 may include one or severalnavigation systems or features including, for example, the GlobalPositioning System (“GPS”), GALILEO (e.g., Europe's global positioningsystem), or the like, or location systems or features including, forexample, one or several transceivers that can determine location of theone or several components of the content distribution network 100 via,for example, triangulation. All of these are depicted as navigationsystem 122.

In some embodiments, navigation system 122 can include or severalfeatures that can communicate with one or several components of thecontent distribution network 100 including, for example, with one orseveral of the user devices 106 and/or with one or several of thesupervisor devices 110. In some embodiments, this communication caninclude the transmission of a signal from the navigation system 122which signal is received by one or several components of the contentdistribution network 100 and can be used to determine the location ofthe one or several components of the content distribution network 100.

With reference to FIG. 2, an illustrative distributed computingenvironment 200 is shown including a computer server 202, four clientcomputing devices 206, and other components that may implement certainembodiments and features described herein. In some embodiments, theserver 202 may correspond to the content management server 102 discussedabove in FIG. 1, and the client computing devices 206 may correspond tothe user devices 106. However, the computing environment 200 illustratedin FIG. 2 may correspond to any other combination of devices and serversconfigured to implement a client-server model or other distributedcomputing architecture.

Client devices 206 may be configured to receive and execute clientapplications over one or more networks 220. Such client applications maybe web browser based applications and/or standalone softwareapplications, such as mobile device applications. Server 202 may becommunicatively coupled with the client devices 206 via one or morecommunication networks 220. Client devices 206 may receive clientapplications from server 202 or from other application providers (e.g.,public or private application stores). Server 202 may be configured torun one or more server software applications or services, for example,web-based or cloud-based services, to support content distribution andinteraction with client devices 206. Users operating client devices 206may in turn utilize one or more client applications (e.g., virtualclient applications) to interact with server 202 to utilize the servicesprovided by these components.

Various different subsystems and/or components 204 may be implemented onserver 202. Users operating the client devices 206 may initiate one ormore client applications to use services provided by these subsystemsand components. The subsystems and components within the server 202 andclient devices 206 may be implemented in hardware, firmware, software,or combinations thereof. Various different system configurations arepossible in different distributed computing systems 200 and contentdistribution networks 100. The embodiment shown in FIG. 2 is thus oneexample of a distributed computing system and is not intended to belimiting.

Although exemplary computing environment 200 is shown with four clientcomputing devices 206, any number of client computing devices may besupported. Other devices, such as specialized sensor devices, etc., mayinteract with client devices 206 and/or server 202.

As shown in FIG. 2, various security and integration components 208 maybe used to send and manage communications between the server 202 anduser devices 206 over one or more communication networks 220. Thesecurity and integration components 208 may include separate servers,such as web servers and/or authentication servers, and/or specializednetworking components, such as firewalls, routers, gateways, loadbalancers, and the like. In some cases, the security and integrationcomponents 208 may correspond to a set of dedicated hardware and/orsoftware operating at the same physical location and under the controlof the same entities as server 202. For example, components 208 mayinclude one or more dedicated web servers and network hardware in adatacenter or a cloud infrastructure. In other examples, the securityand integration components 208 may correspond to separate hardware andsoftware components which may be operated at a separate physicallocation and/or by a separate entity.

Security and integration components 208 may implement various securityfeatures for data transmission and storage, such as authenticating usersand restricting access to unknown or unauthorized users. In variousimplementations, security and integration components 208 may provide,for example, a file-based integration scheme or a service-basedintegration scheme for transmitting data between the various devices inthe content distribution network 100. Security and integrationcomponents 208 also may use secure data transmission protocols and/orencryption for data transfers, for example, File Transfer Protocol(FTP), Secure File Transfer Protocol (SFTP), and/or Pretty Good Privacy(PGP) encryption.

In some embodiments, one or more web services may be implemented withinthe security and integration components 208 and/or elsewhere within thecontent distribution network 100. Such web services, includingcross-domain and/or cross-platform web services, may be developed forenterprise use in accordance with various web service standards, such asRESTful web services (i.e., services based on the Representation StateTransfer (REST) architectural style and constraints), and/or webservices designed in accordance with the Web Service Interoperability(WS-I) guidelines. Some web services may use the Secure Sockets Layer(SSL) or Transport Layer Security (TLS) protocol to provide secureconnections between the server 202 and user devices 206. SSL or TLS mayuse HTTP or HTTPS to provide authentication and confidentiality. Inother examples, web services may be implemented using REST over HTTPSwith the OAuth open standard for authentication, or using theWS-Security standard which provides for secure SOAP (e.g., Simple ObjectAccess Protocol) messages using Extensible Markup Language (XML)encryption. In other examples, the security and integration components208 may include specialized hardware for providing secure web services.For example, security and integration components 208 may include securenetwork appliances having built-in features such as hardware-acceleratedSSL and HTTPS, WS-Security, and firewalls. Such specialized hardware maybe installed and configured in front of any web servers, so that anyexternal devices may communicate directly with the specialized hardware.

Communication network(s) 220 may be any type of network familiar tothose skilled in the art that can support data communications using anyof a variety of commercially-available protocols, including withoutlimitation, TCP/IP (transmission control protocol/Internet protocol),SNA (systems network architecture), IPX (Internet packet exchange),Secure Sockets Layer (SSL) or Transport Layer Security (TLS) protocols,Hyper Text Transfer Protocol (HTTP) and Secure Hyper Text TransferProtocol (HTTPS), Bluetooth®, Near Field Communication (NFC), and thelike. Merely by way of example, network(s) 220 may be local areanetworks (LAN), such as one based on Ethernet, Token-Ring, and/or thelike. Network(s) 220 also may be wide-area networks, such as theInternet. Networks 220 may include telecommunication networks such as apublic switched telephone networks (PSTNs), or virtual networks such asan intranet or an extranet. Infrared and wireless networks (e.g., usingthe Institute of Electrical and Electronics (IEEE) 802.11 protocol suiteor other wireless protocols) also may be included in networks 220.

Computing environment 200 also may include one or more data stores 210and/or back-end servers 212. In certain examples, the data stores 210may correspond to data store server(s) 104 discussed above in FIG. 1,and back-end servers 212 may correspond to the various back-end servers112-116. Data stores 210 and servers 212 may reside in the samedatacenter or may operate at a remote location from server 202. In somecases, one or more data stores 210 may reside on a non-transitorystorage medium within the server 202. Other data stores 210 and back-endservers 212 may be remote from server 202 and configured to communicatewith server 202 via one or more networks 220. In certain embodiments,data stores 210 and back-end servers 212 may reside in a storage-areanetwork (SAN), or may use storage-as-a-service (STaaS) architecturalmodel.

With reference to FIG. 3, an illustrative set of data stores and/or datastore servers is shown, corresponding to the data store servers 104 ofthe content distribution network 100 discussed above in FIG. 1. One ormore individual data stores 301-313 may reside in storage on a singlecomputer server 104 (or a single server farm or cluster) under thecontrol of a single entity, may be virtually implemented, or may resideon separate servers operated by different entities and/or at remotelocations. In some embodiments, data stores 301-313 may be accessed bythe content management server 102 and/or other devices and serverswithin the network 100 (e.g., user devices 106, supervisor devices 110,administrator servers 116, etc.). Access to one or more of the datastores 301-313 may be limited or denied based on the processes, usercredentials, and/or devices attempting to interact with the data store.

The paragraphs below describe examples of specific data stores that maybe implemented within some embodiments of a content distribution network100. It should be understood that the below descriptions of data stores301-313, including their functionality and types of data stored therein,are illustrative and non-limiting. Data stores server architecture,design, and the execution of specific data stores 301-313 may depend onthe context, size, and functional requirements of a content distributionnetwork 100. For example, in content distribution systems 100 used forprofessional training and educational purposes, separate databases orfile-based storage systems may be implemented in data store server(s)104 to store trainee and/or student data, trainer and/or professor data,training module data and content descriptions, training results,evaluation data, and the like. In contrast, in content distributionsystems 100 used for media distribution from content providers tosubscribers, separate data stores may be implemented in data storesserver(s) 104 to store listings of available content titles anddescriptions, content title usage statistics, subscriber profiles,account data, payment data, network usage statistics, etc.

A user profile data store 301, also referred to herein as a user profiledatabase 301, may include information relating to the end users withinthe content distribution network 100. This information may include usercharacteristics such as the user names, access credentials (e.g., loginsand passwords), user preferences, and information relating to anyprevious user interactions within the content distribution network 100(e.g., requested content, posted content, content modules completed,training scores or evaluations, other associated users, etc.). In someembodiments, this information can relate to one or several individualend users such as, for example, one or several students, teachers,administrators, or the like, and in some embodiments, this informationcan relate to one or several institutional end users such as, forexample, one or several schools, groups of schools such as one orseveral school districts, one or several colleges, one or severaluniversities, one or several training providers, or the like. In someembodiments, this information can identify one or several usermemberships in one or several groups such as, for example, a student'smembership in a university, school, program, grade, course, class, orthe like.

The user profile database 301 can include information relating to auser's status, location, or the like. This information can identify, forexample, a device a user is using, the location of that device, or thelike. In some embodiments, this information can be generated based onany location detection technology including, for example, a navigationsystem 122, or the like.

Information relating to the user's status can identify, for example,logged-in status information that can indicate whether the user ispresently logged-in to the content distribution network 100 and/orwhether the log-in is active. In some embodiments, the informationrelating to the user's status can identify whether the user is currentlyaccessing content and/or participating in an activity from the contentdistribution network 100.

In some embodiments, information relating to the user's status canidentify, for example, one or several attributes of the user'sinteraction with the content distribution network 100, and/or contentdistributed by the content distribution network 100. This can includedata identifying the user's interactions with the content distributionnetwork 100, the content consumed by the user through the contentdistribution network 100, or the like. In some embodiments, this caninclude data identifying the type of information accessed through thecontent distribution network 100 and/or the type of activity performedby the user via the content distribution network 100, the lapsed timesince the last time the user accessed content and/or participated in anactivity from the content distribution network 100, or the like. In someembodiments, this information can relate to a content program comprisingan aggregate of data, content, and/or activities, and can identify, forexample, progress through the content program, or through the aggregateof data, content, and/or activities forming the content program. In someembodiments, this information can track, for example, the amount of timesince participation in and/or completion of one or several types ofactivities, the amount of time since communication with one or severalsupervisors and/or supervisor devices 110, or the like.

In some embodiments in which the one or several end users areindividuals, and specifically are students, the user profile database301 can further include information relating to these students' academicand/or educational history. This information can identify one or severalcourses of study that the student has initiated, completed, and/orpartially completed, as well as grades received in those courses ofstudy. In some embodiments, the student's academic and/or educationalhistory can further include information identifying student performanceon one or several tests, quizzes, and/or assignments. In someembodiments, this information can be stored in a tier of memory that isnot the fastest memory in the content delivery network 100. In someembodiments, this can comprise response information such as, forexample, information identifying one or several questions or pieces ofcontent and responses provided to the same. In some embodiments, thisresponse information can be formed into one or several matrices “D”containing information for n users responding top items, these one orseveral matrices D are also referred to herein as the matrix D, the Dmatrix, the user matrix, and/or the response matrix. Thus, the matrix Dcan have n×p dimensions, and in some embodiments, the matrix D canidentify whether user responses to items were correct or incorrect. Insome embodiments, for example, the matrix D can include an entry “1” foran item when a user response to that item is correct and can otherwiseinclude and entry “0”.

The user profile database 301 can include information relating to one orseveral student learning preferences. In some embodiments, for example,the user, also referred to herein as the student or the student-user,may have one or several preferred learning styles, one or several mosteffective learning styles, and/or the like. In some embodiments, theuser's learning style can be any learning style describing how the userbest learns or how the user prefers to learn. In one embodiment, theselearning styles can include, for example, identification of the user asan auditory learner, as a visual learner, and/or as a tactile learner.In some embodiments, the data identifying one or several user learningstyles can include data identifying a learning style based on the user'seducational history such as, for example, identifying a user as anauditory learner when the user has received significantly higher gradesand/or scores on assignments and/or in courses favorable to auditorylearners. In some embodiments, this information can be stored in a tierof memory that is not the fastest memory in the content delivery network100.

In some embodiments, the user profile data store 301 can further includeinformation identifying one or several user skill levels. In someembodiments, these one or several user skill levels can identify a skilllevel determined based on past performance by the user interacting withthe content delivery network 100, and in some embodiments, these one orseveral user skill levels can identify a predicted skill leveldetermined based on past performance by the user interacting with thecontent delivery network 100 and one or several predictive models.

The user profile database 301 can further include information relatingto one or several teachers and/or instructors who are responsible fororganizing, presenting, and/or managing the presentation of informationto the user. In some embodiments, user profile database 301 can includeinformation identifying courses and/or subjects that have been taught bythe teacher, data identifying courses and/or subjects currently taughtby the teacher, and/or data identifying courses and/or subjects thatwill be taught by the teacher. In some embodiments, this can includeinformation relating to one or several teaching styles of one or severalteachers. In some embodiments, the user profile database 301 can furtherinclude information indicating past evaluations and/or evaluationreports received by the teacher. In some embodiments, the user profiledatabase 301 can further include information relating to improvementsuggestions received by the teacher, training received by the teacher,continuing education received by the teacher, and/or the like. In someembodiments, this information can be stored in a tier of memory that isnot the fastest memory in the content delivery network 100.

An accounts data store 302, also referred to herein as an accountsdatabase 302, may generate and store account data for different users invarious roles within the content distribution network 100. For example,accounts may be created in an accounts data store 302 for individual endusers, supervisors, administrator users, and entities such as companiesor educational institutions. Account data may include account types,current account status, account characteristics, and any parameters,limits, restrictions associated with the accounts.

A content library data store 303, also referred to herein as a contentlibrary database 303, may include information describing the individualcontent items (or content resources or data packets) available via thecontent distribution network 100. These data packets can include, forexample, one or several assessment data packets and/or one or severalcontent data packets. In some embodiments, an assessment data packet cancomprise one or several questions for presenting to one or severalusers, in some embodiments, these assessment data packets can be furtherassociated with data for use in evaluating responses received to theassessment data packets, which data can be used to determine whether areceived response is correct, and/or the degree to which the receivedresponse is correct. The content data packets can comprise content forpresenting to one or several users, which content data packets do notinclude assessment data packets. In some embodiments, these data packetsin the content library database 303 can be linked to from an objectnetwork, or specifically to form a Bayes Net content network or learninggraph. In some embodiments, these data packets can be linked in theobject network according to one or several prerequisite relationshipsthat can, for example, identify the relative hierarchy and/or difficultyof the data objects. In some embodiments, this hierarchy of data objectscan be generated by the content distribution network 100 according touser experience with the object network, and in some embodiments, thishierarchy of data objects can be generated based on one or severalexisting and/or external hierarchies such as, for example, a syllabus, atable of contents, or the like. In some embodiments, for example, theobject network can correspond to a syllabus such that content for thesyllabus is embodied in the object network.

In some embodiments, data packets can be associated with a hierarchy,which hierarchy can, for example, correspond to a pre-existing structureof data, such as, for example, table of contents. In some embodiments,for example, the hierarchy can comprise a plurality of positions, whichphysicians can correspond to location a hierarchy. In some embodiments,for example, one position can be the lowest location, the hierarchy,another position can be the highest location in the hierarchy, and otherpositions can have intermediate locations within a hierarchy. In someembodiments, some or all of the data packets can be associated withpositions within the hierarchy. In some embodiments, for example, eachof the content data packets can be associated with a position within thehierarchy, and/or each of the assessment data packets can be associatedwith a position in the hierarchy. In some embodiments, assessment datapacket are associated with a position in the hierarchy such that acorrect response to the assessment data packets indicates mastery ofthat position in the hierarchy.

In some embodiments the content library database can comprise structuresub-database. The structure sub-database can identify a hierarchy thatcan, for example, be generated based on one or several existing and/orexternal hierarchies such as, for example, a syllabus, a table ofcontents, or the like. In some embodiments, some or all of the datapackets within the content library can be each linked with a portionand/or location in the hierarchy. This link can correspond with theportion of the hierarchy with which the data packet is associated. Inone embodiment, for example, in which the data packet is a question, thedata packet can be linked with a portion of the hierarchy, whichhierarchy can correspond to a table of contents, with which the questionis associated. Thus, in some embodiments, the data packet correspondingwith the question can be linked with the portion of the table ofcontents identifying content associated with the question.

In some embodiments, the content library data store 303 can comprise asyllabus, a schedule, or the like. In some embodiments, the syllabus orschedule can identify one or several tasks and/or events relevant to theuser. In some embodiments, for example, when the user is a member of agroup such as, a section or a class, these tasks and/or events relevantto the user can identify one or several assignments, quizzes, exams, orthe like.

In some embodiments, the library data store 303 may include metadata,properties, and other characteristics associated with the contentresources stored in the content server 112. Such data may identify oneor more aspects or content attributes of the associated contentresources, for example, subject matter, access level, or skill level ofthe content resources, license attributes of the content resources(e.g., any limitations and/or restrictions on the licensable use and/ordistribution of the content resource), price attributes of the contentresources (e.g., a price and/or price structure for determining apayment amount for use or distribution of the content resource), ratingattributes for the content resources (e.g., data indicating theevaluation or effectiveness of the content resource), and the like. Insome embodiments, the library data store 303 may be configured to allowupdating of content metadata or properties, and to allow the additionand/or removal of information relating to the content resources. Forexample, content relationships may be implemented as graph structures,which may be stored in the library data store 303 or in an additionalstore for use by selection algorithms along with the other metadata.

In some embodiments, the content library data store 303 can containinformation used in evaluating responses received from users. In someembodiments, for example, a user can receive content from the contentdistribution network 100 and can, subsequent to receiving that content,provide a response to the received content. In some embodiments, forexample, the received content can comprise one or several questions,prompts, or the like, and the response to the received content cancomprise an answer to those one or several questions, prompts, or thelike. In some embodiments, information, referred to herein as“comparative data,” from the content library data store 303 can be usedto determine whether the responses are the correct and/or desiredresponses.

In some embodiments, the content library database 303 and/or the userprofile database 301 can comprise an aggregation network also referredto herein as a content network or content aggregation network. Theaggregation network can comprise a plurality of content aggregationsthat can be linked together by, for example: creation by common user;relation to a common subject, topic, skill, or the like; creation from acommon set of source material such as source data packets; or the like.In some embodiments, the content aggregation can comprise a grouping ofcontent comprising the presentation portion that can be provided to theuser in the form of for example, a flash card and an extraction portionthat can comprise the desired response to the presentation portion suchas for example, an answer to a flash card. In some embodiments, one orseveral content aggregations can be generated by the contentdistribution network 100 and can be related to one or several datapackets they can be, for example, organized in object network. In someembodiments, the one or several content aggregations can be each createdfrom content stored in one or several of the data packets.

In some embodiments, the content aggregations located in the contentlibrary database 303 and/or the user profile database 301 can beassociated with a user-creator of those content aggregations. In someembodiments, access to content aggregations can vary based on, forexample, whether a user created the content aggregations. In someembodiments, the content library database 303 and/or the user profiledatabase 301 can comprise a database of content aggregations associatedwith a specific user, and in some embodiments, the content librarydatabase 303 and/or the user profile database 301 can comprise aplurality of databases of content aggregations that are each associatedwith a specific user. In some embodiments, these databases of contentaggregations can include content aggregations created by their specificuser and in some embodiments, these databases of content aggregationscan further include content aggregations selected for inclusion by theirspecific user and/or a supervisor of that specific user. In someembodiments, these content aggregations can be arranged and/or linked ina hierarchical relationship similar to the data packets in the objectnetwork and/or linked to the object network in the object network or thetasks or skills associated with the data packets in the object networkor the syllabus or schedule.

In some embodiments, the content aggregation network, and the contentaggregations forming the content aggregation network, can be organizedaccording to the object network and/or the hierarchical relationshipsembodied in the object network. In some embodiments, the contentaggregation network, and/or the content aggregations forming the contentaggregation network, can be organized according to one or several tasksidentified in the syllabus, schedule or the like.

A pricing data store 304 may include pricing information and/or pricingstructures for determining payment amounts for providing access to thecontent distribution network 100 and/or the individual content resourceswithin the network 100. In some cases, pricing may be determined basedon a user's access to the content distribution network 100, for example,a time-based subscription fee or pricing based on network usage. Inother cases, pricing may be tied to specific content resources. Certaincontent resources may have associated pricing information, whereas otherpricing determinations may be based on the resources accessed, theprofiles and/or accounts of the user, and the desired level of access(e.g., duration of access, network speed, etc.). Additionally, thepricing data store 304 may include information relating to compilationpricing for groups of content resources, such as group prices and/orprice structures for groupings of resources.

A license data store 305 may include information relating to licensesand/or licensing of the content resources within the contentdistribution network 100. For example, the license data store 305 mayidentify licenses and licensing terms for individual content resourcesand/or compilations of content resources in the content server 112, therights holders for the content resources, and/or common or large-scaleright holder information such as contact information for rights holdersof content not included in the content server 112.

A content access data store 306 may include access rights and securityinformation for the content distribution network 100 and specificcontent resources. For example, the content access data store 306 mayinclude login information (e.g., user identifiers, logins, passwords,etc.) that can be verified during user login attempts to the network100. The content access data store 306 also may be used to storeassigned user roles and/or user levels of access. For example, a user'saccess level may correspond to the sets of content resources and/or theclient or server applications that the user is permitted to access.Certain users may be permitted or denied access to certain applicationsand resources based on their subscription level, training program,course/grade level, etc. Certain users may have supervisory access overone or more end users, allowing the supervisor to access all or portionsof the end user's content, activities, evaluations, etc. Additionally,certain users may have administrative access over some users and/or someapplications in the content management network 100, allowing such usersto add and remove user accounts, modify user access permissions, performmaintenance updates on software and servers, etc.

A source data store 307 may include information relating to the sourceof the content resources available via the content distribution network.For example, a source data store 307 may identify the authors andoriginating devices of content resources, previous pieces of data and/orgroups of data originating from the same authors or originating devicesand the like.

An evaluation data store 308 may include information used to direct theevaluation of users and content resources in the content managementnetwork 100. In some embodiments, the evaluation data store 308 maycontain, for example, the analysis criteria and the analysis guidelinesfor evaluating users (e.g., trainees/students, gaming users, mediacontent consumers, etc.) and/or for evaluating the content resources inthe network 100. The evaluation data store 308 also may includeinformation relating to evaluation processing tasks, for example, theidentification of users and user devices 106 that have received certaincontent resources or accessed certain applications, the status ofevaluations or evaluation histories for content resources, users, orapplications, and the like. Evaluation criteria may be stored in theevaluation data store 308 including data and/or instructions in the formof one or several electronic rubrics or scoring guides for use in theevaluation of the content, users, or applications. The evaluation datastore 308 also may include past evaluations and/or evaluation analysesfor users, content, and applications, including relative rankings,characterizations, explanations, and the like.

A model data store 309, also referred to herein as a model database 309can store information relating to one or several predictive models. Insome embodiments, these can include one or several evidence models, riskmodels, skill models, or the like. In some embodiments, an evidencemodel can be a mathematically-based statistical model. The evidencemodel can be based on, for example, Item Response Theory (IRT), BayesianNetwork (Bayes net), Performance Factor Analysis (PFA), or the like. Theevidence model can, in some embodiments, be customizable to a userand/or to one or several content items. Specifically, one or severalinputs relating to the user and/or to one or several content items canbe inserted into the evidence model. These inputs can include, forexample, one or several measures of user skill level, one or severalmeasures of content item difficulty and/or skill level, or the like. Thecustomized evidence model can then be used to predict the likelihood ofthe user providing desired or undesired responses to one or several ofthe content items.

In some embodiments, the risk models can include one or several modelsthat can be used to calculate one or several model function values. Insome embodiments, these one or several model function values can be usedto calculate a risk probability, which risk probability can characterizethe risk of a student-user failing to achieve a desired outcome such as,for example, failing to correctly respond to one or several datapackets, failure to achieve a desired level of completion of a program,for example in a pre-defined time period, failure to achieve a desiredlearning outcome, or the like. In some embodiments, the risk probabilitycan identify the risk of the student-user failing to complete 60% of theprogram.

In some embodiments, these models can include a plurality of modelfunctions including, for example, a first model function, a second modelfunction, a third model function, and a fourth model function. In someembodiments, some or all of the model functions can be associated with aportion of the program such as, for example a completion stage and/orcompletion status of the program. In one embodiment, for example, thefirst model function can be associated with a first completion status,the second model function can be associated with a second completionstatus, the third model function can be associated with a thirdcompletion status, and the fourth model function can be associated witha fourth completion status. In some embodiments, these completionstatuses can be selected such that some or all of these completionstatuses are less than the desired level of completion of the program.Specifically, in some embodiments, these completion statuses can beselected to all be at less than 60% completion of the program, and morespecifically, in some embodiments, the first completion status can be at20% completion of the program, the second completion status can be at30% completion of the program, the third completion status can be at 40%completion of the program, and the fourth completion status can be at50% completion of the program. Similarly, any desired number of modelfunctions can be associated with any desired number of completionstatuses.

In some embodiments, a model function can be selected from the pluralityof model functions based on a user's progress through a program. In someembodiments, the user's progress can be compared to one or severalstatus trigger thresholds, each of which status trigger thresholds canbe associated with one or more of the model functions. If one of thestatus triggers is triggered by the user's progress, the correspondingone or several model functions can be selected.

The model functions can comprise a variety of types of models and/orfunctions. In some embodiments, each of the model functions outputs afunction value that can be used in calculating a risk probability. Thisfunction value can be calculated by performing one or severalmathematical operations on one or several values indicative of one orseveral user attributes and/or user parameters, also referred to hereinas program status parameters. In some embodiments, each of the modelfunctions can use the same program status parameters, and in someembodiments, the model functions can use different program statusparameters. In some embodiments, the model functions use differentprogram status parameters when at least one of the model functions usesat least one program status parameter that is not used by others of themodel functions.

In some embodiments, a skill model can comprise a statistical modelidentifying a predictive skill level of one or several users. In someembodiments, this model can identify a single skill level of a userand/or a range of possible skill levels of a user. In some embodiments,this statistical model can identify a skill level of a student-user andan error value or error range associated with that skill level. In someembodiments, the error value can be associated with a confidenceinterval determined based on a confidence level. Thus, in someembodiments, as the number of user interactions with the contentdistribution network increases, the confidence level can increase andthe error value can decrease such that the range identified by the errorvalue about the predicted skill level is smaller.

In some embodiments, the model database 309, can further include datacharacterizing one or several attributes of one or several of the modelstored in the model database. In some embodiments, this data cancharacterize aspects of the training of one or several of the modelstored in the model database including, for example, identification ofone or several sets of training data, identification of attributes ofone or several sets of training data, such as, for example, the size ofthe sets of training data, or the like. In some embodiments, this datacan further include data characterizing the confidence of one or severalmodels stored in the model database 309.

A threshold database 310 can store one or several threshold values.These one or several threshold values can delineate between states orconditions. In one exemplary embodiment, for example, a threshold valuecan delineate between an acceptable user performance and an unacceptableuser performance, between content appropriate for a user and contentthat is inappropriate for a user, between risk levels, or the like.

A prioritization database 311 can include data relating to one orseveral tasks and the prioritization of those one or several tasks withrespect to each other. In some embodiments, the prioritization database311 can be unique to a specific user, and in some embodiments, theprioritization database 311 can be applicable to a plurality of users.In some embodiments in which the prioritization database 311 is uniqueto a specific user, the prioritization database 311 can be asub-database of the user profile database 301. In some embodiments, theprioritization database 311 can include information identifying aplurality of tasks and a relative prioritization amongst that pluralityof tasks. In some embodiments, this prioritization can be static and insome embodiments, this prioritization can be dynamic in that theprioritization can change based on updates, for example, one or severalof the tasks, the user profile database 301, or the like. In someembodiments, the prioritization database 311 can include informationrelating to tasks associated with a single course, group, class, or thelike, and in some embodiments, the prioritization database 311 caninclude information relating to tasks associated with a plurality ofcourses, groups, classes, or the like.

A task can define an objective and/or outcome and can be associated withone or several data packets that can, for example, contribute to userattainment of the objective and/or outcome. In some embodiments, some orall of the data packets contained in the content library database 303can be linked with one or several tasks stored in the prioritizationdatabase 311 such that a single task can be linked and/or associatedwith one or several data packets.

The prioritization database 311 can further include information relevantto the prioritization of one or several tasks and/or the prioritizationdatabase 311 can include information that can be used in determining theprioritization of one or several tasks. In some embodiments, this caninclude weight data which can identify a relative and/or absolute weightof a task. In some embodiments, for example, the weight data canidentify the degree to which a task contributes to an outcome such as,for example, a score or a grade. In some embodiments, this weight datacan specify the portion and/or percent of a grade of a class, section,course, or study that results from, and/or that is associated with thetask.

The prioritization database 311 can further include information relevantto the composition of the task. In some embodiments, for example, thisinformation, also referred to herein as a composition value, canidentify one or several sub-tasks and/or content categories forming thetasks, as well as a contribution of each of those sub-tasks and/orcontent categories to the task. In some embodiments, the application ofthe weight data to the composition value can result in theidentification of a contribution value for the task and/or for the oneor several sub-tasks and/or content categories forming the task. Thiscontribution value can identify the contribution of one, some, or all ofthe sub-tasks and/or content categories to the outcome such as, forexample, the score or the grade.

The calendar data source 312, also referred to herein as the calendardatabase 312 can include timing information relevant to the taskscontained in the prioritization database 311. In some embodiments, thistiming information can identify one or several dates by which the tasksshould be completed, one or several event dates associated with the tasksuch as, for example, one or several due dates, test dates, or the like,holiday information, or the like. In some embodiments, the calendardatabase 312 can further include any information provided to the userrelating to other goals, commitments, or the like.

In addition to the illustrative data stores described above, data storeserver(s) 104 (e.g., database servers, file-based storage servers, etc.)may include one or more external data aggregators 313. External dataaggregators 313 may include third-party data sources accessible to thecontent management network 100, but not maintained by the contentmanagement network 100. External data aggregators 313 may include anyelectronic information source relating to the users, content resources,or applications of the content distribution network 100. For example,external data aggregators 313 may be third-party data stores containingdemographic data, education related data, consumer sales data, healthrelated data, and the like. Illustrative external data aggregators 313may include, for example, social networking web servers, public recordsdata stores, learning management systems, educational institutionservers, business servers, consumer sales data stores, medical recorddata stores, etc. Data retrieved from various external data aggregators313 may be used to verify and update user account information, suggestuser content, and perform user and content evaluations.

With reference now to FIG. 4, a block diagram is shown illustrating anembodiment of one or more content management servers 102 within acontent distribution network 100. In such an embodiment, contentmanagement server 102 performs internal data gathering and processing ofstreamed content along with external data gathering and processing.Other embodiments could have either all external or all internal datagathering. This embodiment allows reporting timely information thatmight be of interest to the reporting party or other parties. In thisembodiment, the content management server 102 can monitor gatheredinformation from several sources to allow it to make timely businessand/or processing decisions based upon that information. For example,reports of user actions and/or responses, as well as the status and/orresults of one or several processing tasks could be gathered andreported to the content management server 102 from a number of sources.

Internally, the content management server 102 gathers information fromone or more internal components 402-408. The internal components 402-408gather and/or process information relating to such things as: contentprovided to users; content consumed by users; responses provided byusers; user skill levels; content difficulty levels; next content forproviding to users; etc. The internal components 402-408 can report thegathered and/or generated information in real-time, near real-time oralong another time line. To account for any delay in reportinginformation, a time stamp or staleness indicator can inform others ofhow timely the information was sampled. The content management server102 can opt to allow third parties to use internally or externallygathered information that is aggregated within the server 102 bysubscription to the content distribution network 100.

A command and control (CC) interface 338 configures the gathered inputinformation to an output of data streams, also referred to herein ascontent streams. APIs for accepting gathered information and providingdata streams are provided to third parties external to the server 102who want to subscribe to data streams. The server 102 or a third partycan design as yet undefined APIs using the CC interface 338. The server102 can also define authorization and authentication parameters usingthe CC interface 338 such as authentication, authorization, login,and/or data encryption. CC information is passed to the internalcomponents 402-408 and/or other components of the content distributionnetwork 100 through a channel separate from the gathered information ordata stream in this embodiment, but other embodiments could embed CCinformation in these communication channels. The CC information allowsthrottling information reporting frequency, specifying formats forinformation and data streams, deactivation of one or several internalcomponents 402-408 and/or other components of the content distributionnetwork 100, updating authentication and authorization, etc.

The various data streams that are available can be researched andexplored through the CC interface 338. Those data stream selections fora particular subscriber, which can be one or several of the internalcomponents 402-408 and/or other components of the content distributionnetwork 100, are stored in the queue subscription information database322. The server 102 and/or the CC interface 338 then routes selecteddata streams to processing subscribers that have selected delivery of agiven data stream. Additionally, the server 102 also supports historicalqueries of the various data streams that are stored in an historicaldata store 334 as gathered by an archive data agent 336. Through the CCinterface 338 various data streams can be selected for archiving intothe historical data store 334.

Components of the content distribution network 100 outside of the server102 can also gather information that is reported to the server 102 inreal-time, near real-time, or along another time line. There is adefined API between those components and the server 102. Each type ofinformation or variable collected by server 102 falls within a definedAPI or multiple APIs. In some cases, the CC interface 338 is used todefine additional variables to modify an API that might be of use toprocessing subscribers. The additional variables can be passed to allprocessing subscribes or just a subset. For example, a component of thecontent distribution network 100 outside of the server 102 may report auser response, but define an identifier of that user as a privatevariable that would not be passed to processing subscribers lackingaccess to that user and/or authorization to receive that user data.Processing subscribers having access to that user and/or authorizationto receive that user data would receive the subscriber identifier alongwith the response reported to that component. Encryption and/or uniqueaddressing of data streams or sub-streams can be used to hide theprivate variables within the messaging queues.

The user devices 106 and/or supervisor devices 110 communicate with theserver 102 through security and/or integration hardware 410. Thecommunication with security and/or integration hardware 410 can beencrypted or not. For example, a socket using a TCP connection could beused. In addition to TCP, other transport layer protocols like ControlTransmission Protocol (SCTP) and User Datagram Protocol (UDP) could beused in some embodiments to intake the gathered information. A protocolsuch as SSL could be used to protect the information over the TCPconnection. Authentication and authorization can be performed to anyuser devices 106 and/or supervisor device interfacing to the server 102.The security and/or integration hardware 410 receives the informationfrom one or several of the user devices 106 and/or the supervisordevices 110 by providing the API and any encryption, authorization,and/or authentication. In some cases, the security and/or integrationhardware 410 reformats or rearranges this received information

The messaging bus 412, also referred to herein as a messaging queue or amessaging channel, can receive information from the internal componentsof the server 102 and/or components of the content distribution network100 outside of the server 102 and distribute the gathered information asa data stream to any processing subscribers that have requested the datastream from the messaging queue 412. As indicated in FIG. 4, processingsubscribers are indicated by a connector to the messaging bus 412, theconnector having an arrow head pointing away from the messaging bus 412.In some examples, only data streams within the messaging queue 412 thata particular processing subscriber has subscribed to may be read by thatprocessing subscriber if received at all. Gathered information sent tothe messaging queue 412 is processed and returned in a data stream in afraction of a second by the messaging queue 412. Various multicastingand routing techniques can be used to distribute a data stream from themessaging queue 412 that a number of processing subscribers haverequested. Protocols such as Multicast or multiple Unicast could be usedto distributed streams within the messaging queue 412. Additionally,transport layer protocols like TCP, SCTP and UDP could be used invarious embodiments.

Through the CC interface 338, an external or internal processingsubscriber can be assigned one or more data streams within the messagingqueue 412. A data stream is a particular type of messages in aparticular category. For example, a data stream can comprise all of thedata reported to the messaging bus 412 by a designated set ofcomponents. One or more processing subscribers could subscribe andreceive the data stream to process the information and make a decisionand/or feed the output from the processing as gathered information fedback into the messaging queue 412. Through the CC interface 338 adeveloper can search the available data streams or specify a new datastream and its API. The new data stream might be determined byprocessing a number of existing data streams with a processingsubscriber.

The CDN 110 has internal processing subscribers 402-408 that processassigned data streams to perform functions within the server 102.Internal processing subscribers 402-408 could perform functions such asproviding content to a user, receiving a response from a user,determining the correctness of the received response, updating one orseveral models based on the correctness of the response, recommendingnew content for providing to one or several users, or the like. Theinternal processing subscribers 402-408 can decide filtering andweighting of records from the data stream. To the extent that decisionsare made based upon analysis of the data stream, each data record istime stamped to reflect when the information was gathered such thatadditional credibility could be given to more recent results, forexample. Other embodiments may filter out records in the data streamthat are from an unreliable source or stale. For example, a particularcontributor of information may prove to have less than optimal gatheredinformation and that could be weighted very low or removed altogether.

Internal processing subscribers 402-408 may additionally process one ormore data streams to provide different information to feed back into themessaging queue 412 to be part of a different data stream. For example,hundreds of user devices 106 could provide responses that are put into adata stream on the messaging queue 412. An internal processingsubscriber 402-408 could receive the data stream and process it todetermine the difficulty of one or several data packets provided to oneor several users and supply this information back onto the messagingqueue 412 for possible use by other internal and external processingsubscribers.

As mentioned above, the CC interface 338 allows the CDN 110 to queryhistorical messaging queue 412 information. An archive data agent 336listens to the messaging queue 412 to store data streams in a historicaldatabase 334. The historical database 334 may store data streams forvarying amounts of time and may not store all data streams. Differentdata streams may be stored for different amounts of time.

With regards to the components 402-408, the content management server(s)102 may include various server hardware and software components thatmanage the content resources within the content distribution network 100and provide interactive and adaptive content to users on various userdevices 106. For example, content management server(s) 102 may provideinstructions to and receive information from the other devices withinthe content distribution network 100, in order to manage and transmitcontent resources, user data, and server or client applicationsexecuting within the network 100.

A content management server 102 may include a packet selection system402. The packet selection system 402 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., a packetselection server 402), or using designated hardware and softwareresources within a shared content management server 102. In someembodiments, the packet selection system 402 may adjust the selectionand adaptive capabilities of content resources to match the needs anddesires of the users receiving the content. For example, the packetselection system 402 may query various data stores and servers 104 toretrieve user information, such as user preferences and characteristics(e.g., from a user profile data store 301), user access restrictions tocontent recourses (e.g., from a content access data store 306), previoususer results and content evaluations (e.g., from an evaluation datastore 308), and the like. Based on the retrieved information from datastores 104 and other data sources, the packet selection system 402 maymodify content resources for individual users.

In some embodiments, the packet selection system 402 can include arecommendation engine, also referred to herein as an adaptiverecommendation engine. In some embodiments, the recommendation enginecan select one or several pieces of content, also referred to herein asdata packets, for providing to a user. These data packets can beselected based on, for example, the information retrieved from thedatabase server 104 including, for example, the user profile database301, the content library database 303, the model database 309, or thelike. In some embodiments, these one or several data packets can beadaptively selected and/or selected according to one or severalselection rules. In one embodiment, for example, the recommendationengine can retrieve information from the user profile database 301identifying, for example, a skill level of the user. The recommendationengine can further retrieve information from the content librarydatabase 303 identifying, for example, potential data packets forproviding to the user and the difficulty of those data packets and/orthe skill level associated with those data packets.

The recommendation engine can identify one or several potential datapackets for providing and/or one or several data packets for providingto the user based on, for example, one or several rules, models,predictions, or the like. The recommendation engine can use the skilllevel of the user to generate a prediction of the likelihood of one orseveral users providing a desired response to some or all of thepotential data packets. In some embodiments, the recommendation enginecan pair one or several data packets with selection criteria that may beused to determine which packet should be delivered to a user based onone or several received responses from that student-user. In someembodiments, one or several data packets can be eliminated from the poolof potential data packets if the prediction indicates either too high alikelihood of a desired response or too low a likelihood of a desiredresponse. In some embodiments, the recommendation engine can then applyone or several selection criteria to the remaining potential datapackets to select a data packet for providing to the user. These one orseveral selection criteria can be based on, for example, criteriarelating to a desired estimated time for receipt of response to the datapacket, one or several content parameters, one or several assignmentparameters, or the like.

A content management server 102 also may include a summary model system404. The summary model system 404 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., a summarymodel server 404), or using designated hardware and software resourceswithin a shared content management server 102. In some embodiments, thesummary model system 404 may monitor the progress of users throughvarious types of content resources and groups, such as mediacompilations, courses, or curriculums in training or educationalcontexts, interactive gaming environments, and the like. For example,the summary model system 404 may query one or more databases and/or datastore servers 104 to retrieve user data such as associated contentcompilations or programs, content completion status, user goals,results, and the like.

A content management server 102 also may include a response system 406,which can include, in some embodiments, a response processor. Theresponse system 406 may be implemented using dedicated hardware withinthe content distribution network 100 (e.g., a response server 406), orusing designated hardware and software resources within a shared contentmanagement server 102. The response system 406 may be configured toreceive and analyze information from user devices 106. For example,various ratings of content resources submitted by users may be compiledand analyzed, and then stored in a data store (e.g., a content librarydata store 303 and/or evaluation data store 308) associated with thecontent. In some embodiments, the response server 406 may analyze theinformation to determine the effectiveness or appropriateness of contentresources with, for example, a subject matter, an age group, a skilllevel, or the like. In some embodiments, the response system 406 mayprovide updates to the packet selection system 402 or the summary modelsystem 404, with the attributes of one or more content resources orgroups of resources within the network 100. The response system 406 alsomay receive and analyze user evaluation data from user devices 106,supervisor devices 110, and administrator servers 116, etc. Forinstance, response system 406 may receive, aggregate, and analyze userevaluation data for different types of users (e.g., end users,supervisors, administrators, etc.) in different contexts (e.g., mediaconsumer ratings, trainee or student comprehension levels, teachereffectiveness levels, gamer skill levels, etc.).

In some embodiments, the response system 406 can be further configuredto receive one or several responses from the user and analyze these oneor several responses. In some embodiments, for example, the responsesystem 406 can be configured to translate the one or several responsesinto one or several observables. As used herein, an observable is acharacterization of a received response. In some embodiments, thetranslation of the one or several response into one or severalobservables can include determining whether the one or several responseare correct responses, also referred to herein as desired responses, orare incorrect responses, also referred to herein as undesired responses.In some embodiments, the translation of the one or several response intoone or several observables can include characterizing the degree towhich one or several response are desired responses and/or undesiredresponses. In some embodiments, one or several values can be generatedby the response system 406 to reflect user performance in responding tothe one or several data packets. In some embodiments, these one orseveral values can comprise one or several scores for one or severalresponses and/or data packets.

A content management server 102 also may include a presentation system408. The presentation system 408 may be implemented using dedicatedhardware within the content distribution network 100 (e.g., apresentation server 408), or using designated hardware and softwareresources within a shared content management server 102. Thepresentation system 408 can include a presentation engine that can be,for example, a software module running on the content delivery system.

The presentation system 408, also referred to herein as the presentationmodule or the presentation engine, may receive content resources fromthe packet selection system 402 and/or from the summary model system404, and provide the resources to user devices 106. The presentationsystem 408 may determine the appropriate presentation format for thecontent resources based on the user characteristics and preferences,and/or the device capabilities of user devices 106. If needed, thepresentation system 408 may convert the content resources to theappropriate presentation format and/or compress the content beforetransmission. In some embodiments, the presentation system 408 may alsodetermine the appropriate transmission media and communication protocolsfor transmission of the content resources.

In some embodiments, the presentation system 408 may include specializedsecurity and integration hardware 410, along with corresponding softwarecomponents to implement the appropriate security features contenttransmission and storage, to provide the supported network and clientaccess models, and to support the performance and scalabilityrequirements of the network 100. The security and integration layer 410may include some or all of the security and integration components 208discussed above in FIG. 2, and may control the transmission of contentresources and other data, as well as the receipt of requests and contentinteractions, to and from the user devices 106, supervisor devices 110,administrator servers 116, and other devices in the network 100.

With reference now to FIG. 5, a block diagram of an illustrativecomputer system is shown. The system 500 may correspond to any of thecomputing devices or servers of the content distribution network 100described above, or any other computing devices described herein, andspecifically can include, for example, one or several of the userdevices 106, the supervisor device 110, and/or any of the servers 102,104, 108, 112, 114, 116. In this example, computer system 500 includesprocessing units 504 that communicate with a number of peripheralsubsystems via a bus subsystem 502. These peripheral subsystems include,for example, a storage subsystem 510, an I/O subsystem 526, and acommunications subsystem 532.

Bus subsystem 502 provides a mechanism for letting the variouscomponents and subsystems of computer system 500 communicate with eachother as intended. Although bus subsystem 502 is shown schematically asa single bus, alternative embodiments of the bus subsystem may utilizemultiple buses. Bus subsystem 502 may be any of several types of busstructures including a memory bus or memory controller, a peripheralbus, and a local bus using any of a variety of bus architectures. Sucharchitectures may include, for example, an Industry StandardArchitecture (ISA) bus, Micro Channel Architecture (MCA) bus, EnhancedISA (EISA) bus, Video Electronics Standards Association (VESA) localbus, and Peripheral Component Interconnect (PCI) bus, which can beimplemented as a Mezzanine bus manufactured to the IEEE P1386.1standard.

Processing unit 504, which may be implemented as one or more integratedcircuits (e.g., a conventional microprocessor or microcontroller),controls the operation of computer system 500. One or more processors,including single core and/or multicore processors, may be included inprocessing unit 504. As shown in the figure, processing unit 504 may beimplemented as one or more independent processing units 506 and/or 508with single or multicore processors and processor caches included ineach processing unit. In other embodiments, processing unit 504 may alsobe implemented as a quad-core processing unit or larger multicoredesigns (e.g., hexa-core processors, octo-core processors, ten-coreprocessors, or greater.

Processing unit 504 may execute a variety of software processes embodiedin program code, and may maintain multiple concurrently executingprograms or processes. At any given time, some or all of the programcode to be executed can be resident in processor(s) 504 and/or instorage subsystem 510. In some embodiments, computer system 500 mayinclude one or more specialized processors, such as digital signalprocessors (DSPs), outboard processors, graphics processors,application-specific processors, and/or the like.

I/O subsystem 526 may include device controllers 528 for one or moreuser interface input devices and/or user interface output devices 530.User interface input and output devices 530 may be integral with thecomputer system 500 (e.g., integrated audio/video systems, and/ortouchscreen displays), or may be separate peripheral devices which areattachable/detachable from the computer system 500. The I/O subsystem526 may provide one or several outputs to a user by converting one orseveral electrical signals to user perceptible and/or interpretableforin, and may receive one or several inputs from the user by generatingone or several electrical signals based on one or several user-causedinteractions with the I/O subsystem such as the depressing of a key orbutton, the moving of a mouse, the interaction with a touchscreen ortrackpad, the interaction of a sound wave with a microphone, or thelike.

Input devices 530 may include a keyboard, pointing devices such as amouse or trackball, a touchpad or touch screen incorporated into adisplay, a scroll wheel, a click wheel, a dial, a button, a switch, akeypad, audio input devices with voice command recognition systems,microphones, and other types of input devices. Input devices 530 mayalso include three dimensional (3D) mice, joysticks or pointing sticks,gamepads and graphic tablets, and audio/visual devices such as speakers,digital cameras, digital camcorders, portable media players, webcams,image scanners, fingerprint scanners, barcode reader 3D scanners, 3Dprinters, laser rangefinders, and eye gaze tracking devices. Additionalinput devices 530 may include, for example, motion sensing and/orgesture recognition devices that enable users to control and interactwith an input device through a natural user interface using gestures andspoken commands, eye gesture recognition devices that detect eyeactivity from users and transform the eye gestures as input into aninput device, voice recognition sensing devices that enable users tointeract with voice recognition systems through voice commands, medicalimaging input devices, MIDI keyboards, digital musical instruments, andthe like.

Output devices 530 may include one or more display subsystems, indicatorlights, or non-visual displays such as audio output devices, etc.Display subsystems may include, for example, cathode ray tube (CRT)displays, flat-panel devices, such as those using a liquid crystaldisplay (LCD) or plasma display, light-emitting diode (LED) displays,projection devices, touch screens, and the like. In general, use of theterm “output device” is intended to include all possible types ofdevices and mechanisms for outputting information from computer system500 to a user or other computer. For example, output devices 530 mayinclude, without limitation, a variety of display devices that visuallyconvey text, graphics, and audio/video information such as monitors,printers, speakers, headphones, automotive navigation systems, plotters,voice output devices, and modems.

Computer system 500 may comprise one or more storage subsystems 510,comprising hardware and software components used for storing data andprogram instructions, such as system memory 518 and computer-readablestorage media 516. The system memory 518 and/or computer-readablestorage media 516 may store program instructions that are loadable andexecutable on processing units 504, as well as data generated during theexecution of these programs.

Depending on the configuration and type of computer system 500, systemmemory 518 may be stored in volatile memory (such as random accessmemory (RAM) 512) and/or in non-volatile storage drives 514 (such asread-only memory (ROM), flash memory, etc.). The RAM 512 may containdata and/or program modules that are immediately accessible to and/orpresently being operated and executed by processing units 504. In someimplementations, system memory 518 may include multiple different typesof memory, such as static random access memory (SRAM) or dynamic randomaccess memory (DRAM). In some implementations, a basic input/outputsystem (BIOS), containing the basic routines that help to transferinformation between elements within computer system 500, such as duringstart-up, may typically be stored in the non-volatile storage drives514. By way of example, and not limitation, system memory 518 mayinclude application programs 520, such as client applications, Webbrowsers, mid-tier applications, server applications, etc., program data522, and an operating system 524.

Storage subsystem 510 also may provide one or more tangiblecomputer-readable storage media 516 for storing the basic programmingand data constructs that provide the functionality of some embodiments.Software (programs, code modules, instructions) that when executed by aprocessor provide the functionality described herein may be stored instorage subsystem 510. These software modules or instructions may beexecuted by processing units 504. Storage subsystem 510 may also providea repository for storing data used in accordance with the presentinvention.

Storage subsystem 510 may also include a computer-readable storage mediareader that can further be connected to computer-readable storage media516. Together and, optionally, in combination with system memory 518,computer-readable storage media 516 may comprehensively representremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containing, storing,transmitting, and retrieving computer-readable information.

Computer-readable storage media 516 containing program code, or portionsof program code, may include any appropriate media known or used in theart, including storage media and communication media, such as, but notlimited to, volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information. This can include tangible computer-readable storagemedia such as RAM, ROM, electronically erasable programmable ROM(EEPROM), flash memory or other memory technology, CD-ROM, digitalversatile disk (DVD), or other optical storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or other tangible computer readable media. This can also includenontangible computer-readable media, such as data signals, datatransmissions, or any other medium which can be used to transmit thedesired information and which can be accessed by computer system 500.

By way of example, computer-readable storage media 516 may include ahard disk drive that reads from or writes to non-removable, nonvolatilemagnetic media, a magnetic disk drive that reads from or writes to aremovable, nonvolatile magnetic disk, and an optical disk drive thatreads from or writes to a removable, nonvolatile optical disk such as aCD ROM, DVD, and Blu-Ray® disk, or other optical media.Computer-readable storage media 516 may include, but is not limited to,Zip® drives, flash memory cards, universal serial bus (USB) flashdrives, secure digital (SD) cards, DVD disks, digital video tape, andthe like. Computer-readable storage media 516 may also include,solid-state drives (SSD) based on non-volatile memory such asflash-memory based SSDs, enterprise flash drives, solid state ROM, andthe like, SSDs based on volatile memory such as solid state RAM, dynamicRAM, static RAM, DRAM-based SSDs, magnetoresistive RAM (MRAM) SSDs, andhybrid SSDs that use a combination of DRAM and flash memory based SSDs.The disk drives and their associated computer-readable media may providenon-volatile storage of computer-readable instructions, data structures,program modules, and other data for computer system 500.

Communications subsystem 532 may provide a communication interface fromcomputer system 500 and external computing devices via one or morecommunication networks, including local area networks (LANs), wide areanetworks (WANs) (e.g., the Internet), and various wirelesstelecommunications networks. As illustrated in FIG. 5, thecommunications subsystem 532 may include, for example, one or morenetwork interface controllers (NICs) 534, such as Ethernet cards,Asynchronous Transfer Mode NICs, Token Ring NICs, and the like, as wellas one or more wireless communications interfaces 536, such as wirelessnetwork interface controllers (WNICs), wireless network adapters, andthe like. As illustrated in FIG. 5, the communications subsystem 532 mayinclude, for example, one or more location determining features 538 suchas one or several navigation system features and/or receivers, and thelike.

Additionally and/or alternatively, the communications subsystem 532 mayinclude one or more modems (telephone, satellite, cable, ISDN),synchronous or asynchronous digital subscriber line (DSL) units,FireWire® interfaces, USB® interfaces, and the like. Communicationssubsystem 536 also may include radio frequency (RF) transceivercomponents for accessing wireless voice and/or data networks (e.g.,using cellular telephone technology, advanced data network technology,such as 3G, 4G or EDGE (enhanced data rates for global evolution), WiFi(IEEE 802.11 family standards, or other mobile communicationtechnologies, or any combination thereof), global positioning system(GPS) receiver components, and/or other components.

The various physical components of the communications subsystem 532 maybe detachable components coupled to the computer system 500 via acomputer network, a FireWire® bus, or the like, and/or may be physicallyintegrated onto a motherboard of the computer system 500. Communicationssubsystem 532 also may be implemented in whole or in part by software.

In some embodiments, communications subsystem 532 may also receive inputcommunication in the form of structured and/or unstructured data feeds,event streams, event updates, and the like, on behalf of one or moreusers who may use or access computer system 500. For example,communications subsystem 532 may be configured to receive data feeds inreal-time from users of social networks and/or other communicationservices, web feeds such as Rich Site Summary (RSS) feeds, and/orreal-time updates from one or more third party information sources(e.g., external data source 313). Additionally, communications subsystem532 may be configured to receive data in the form of continuous datastreams, which may include event streams of real-time events and/orevent updates (e.g., sensor data applications, financial tickers,network performance measuring tools, clickstream analysis tools,automobile traffic monitoring, etc.). Communications subsystem 532 mayoutput such structured and/or unstructured data feeds, event streams,event updates, and the like to one or more data stores 104 that may bein communication with one or more streaming data source computerscoupled to computer system 500.

Due to the ever-changing nature of computers and networks, thedescription of computer system 500 depicted in the figure is intendedonly as a specific example. Many other configurations having more orfewer components than the system depicted in the figure are possible.For example, customized hardware might also be used and/or particularelements might be implemented in hardware, firmware, software, or acombination. Further, connection to other computing devices, such asnetwork input/output devices, may be employed. Based on the disclosureand teachings provided herein, a person of ordinary skill in the artwill appreciate other ways and/or methods to implement the variousembodiments.

With reference now to FIG. 6, a block diagram illustrating oneembodiment of the communication network is shown. Specifically, FIG. 6depicts one hardware configuration in which messages are exchangedbetween a source hub 602 and a terminal hub 606 via the communicationnetwork 120 that can include one or several intermediate hubs 604. Insome embodiments, the source hub 602 can be any one or severalcomponents of the content distribution network generating and initiatingthe sending of a message, and the terminal hub 606 can be any one orseveral components of the content distribution network 100 receiving andnot re-sending the message. In some embodiments, for example, the sourcehub 602 can be one or several of the user device 106, the supervisordevice 110, and/or the server 102, and the terminal hub 606 can likewisebe one or several of the user device 106, the supervisor device 110,and/or the server 102. In some embodiments, the intermediate hubs 604can include any computing device that receives the message and resendsthe message to a next node.

As seen in FIG. 6, in some embodiments, each of the hubs 602, 604, 606can be communicatively connected with the data store 104. In such anembodiments, some or all of the hubs 602, 604, 606 can send informationto the data store 104 identifying a received message and/or any sent orresent message. This information can, in some embodiments, be used todetermine the completeness of any sent and/or received messages and/orto verify the accuracy and completeness of any message received by theterminal hub 606.

In some embodiments, the communication network 120 can be formed by theintermediate hubs 604. In some embodiments, the communication network120 can comprise a single intermediate hub 604, and in some embodiments,the communication network 120 can comprise a plurality of intermediatehubs. In one embodiment, for example, and as depicted in FIG. 6, thecommunication network 120 includes a first intermediate hub 604-A and asecond intermediate hub 604-B.

With reference now to FIG. 7, a block diagram illustrating oneembodiment of user device 106 and supervisor device 110 communication isshown. In some embodiments, for example, a user may have multipledevices that can connect with the content distribution network 100 tosend or receive information. In some embodiments, for example, a usermay have a personal device such as a mobile device, a smartphone, atablet, a smartwatch, a laptop, a PC, or the like. In some embodiments,the other device can be any computing device in addition to the personaldevice. This other device can include, for example, a laptop, a PC, asmartphone, a tablet, a smartwatch, or the like. In some embodiments,the other device differs from the personal device in that the personaldevice is registered as such within the content distribution network 100and the other device is not registered as a personal device within thecontent distribution network 100.

Specifically with respect to FIG. 7 in view of the devices illustratedwith FIG. 1, the user device 106 can include a personal user device106-A and one or several other user devices 106-B. In some embodiments,one or both of the personal user device 106-A and the one or severalother user devices 106-B can be communicatively connected to the contentmanagement server 102 and/or to the navigation system 122. Similarly,the supervisor device 110 can include a personal supervisor device 110-Aand one or several other supervisor devices 110-B. In some embodiments,one or both of the personal supervisor device 110-A and the one orseveral other supervisor devices 110-B can be communicatively connectedto the content management server 102 and/or to the navigation system122.

In some embodiments, the content distribution network can send one ormore alerts to one or more user devices 106 and/or one or moresupervisor devices 110 via, for example, the communication network 120.In some embodiments, the receipt of the alert can result in thelaunching of an application within the receiving device, and in someembodiments, the alert can include a link that, when selected, launchesthe application or navigates a web-browser of the device of the selectorof the link to page or portal associated with the alert.

In some embodiments, for example, the providing of this alert caninclude the identification of one or several user devices 106 and/orstudent-user accounts associated with the student-user and/or one orseveral supervisor devices 110 and/or supervisor-user accountsassociated with the supervisor-user. After these one or several devices106, 110 and/or accounts have been identified, the providing of thisalert can include determining an active device of the devices 106, 110based on determining which of the devices 106, 110 and/or accounts areactively being used, and then providing the alert to that active device.

Specifically, if the user is actively using one of the devices 106, 110such as the other user device 106-B and the other supervisor device110-B, and/or accounts, the alert can be provided to the user via thatother device 106-B, 110-B, and/or account that is actively being used.If the user is not actively using another device 106-B, 110-B, and/oraccount, a personal device 106-A, 110-A device, such as a smart phone ortablet, can be identified and the alert can be provided to this personaldevice 106-A, 110-A. In some embodiments, the alert can include code todirect the default device to provide an indicator of the received alertsuch as, for example, an oral, tactile, or visual indicator of receiptof the alert.

In some embodiments, the recipient device 106, 110 of the alert canprovide an indication of receipt of the alert. In some embodiments, thepresentation of the alert can include the control of the I/O subsystem526 to, for example, provide an oral, tactile, and/or visual indicatorof the alert and/or of the receipt of the alert. In some embodiments,this can include controlling a screen of the supervisor device 110 todisplay the alert, data contained in alert and/or an indicator of thealert.

With reference now to FIG. 8, a schematic illustration of one embodimentof an application stack, and particularly of a stack 650 is shown. Insome embodiments, the content distribution network 100 can comprise aportion of the stack 650 that can include an infrastructure layer 652, aplatform layer 654, an applications layer 656, and a products layer 658.In some embodiments, the stack 650 can comprise some or all of thelayers, hardware, and/or software to provide one or several desiredfunctionalities and/or productions.

As depicted in FIG. 8, the infrastructure layer 652 can include one orseveral servers, communication networks, data stores, privacy servers,and the like. In some embodiments, the infrastructure layer can furtherinclude one or several user devices 106 and/or supervisor devices 110connected as part of the content distribution network.

The platform layer can include one or several platform softwareprograms, modules, and/or capabilities. These can include, for example,identification services, security services, and/or adaptive platformservices 660. In some embodiments, the identification services can, forexample, identify one or several users, components of the contentdistribution network 100, or the like. The security services can monitorthe content distribution network for one or several security threats,breaches, viruses, malware, or the like. The adaptive platform services660 can receive information from one or several components of thecontent distribution network 100 and can provide predictions, models,recommendations, or the like based on that received information. Thefunctionality of the adaptive platform services 660 will be discussed ingreater detail in FIGS. 9-11, below.

The applications layer 656 can include software or software modules uponor in which one or several product softwares or product software modulescan operate. In some embodiments, the applications layer 656 caninclude, for example, a management system, record system, or the like.In some embodiments, the management system can include, for example, aLearning Management System (LMS), a Content Management System (CMS), orthe like. The management system can be configured to control thedelivery of one or several resources to a user and/or to receive one orseveral responses from the user. In some embodiments, the records systemcan include, for example, a virtual gradebook, a virtual counselor, orthe like.

The products layer can include one or several software products and/orsoftware module products. These software products and/or software moduleproducts can provide one or several services and/or functionalities toone or several users of the software products and/or software moduleproducts.

With reference now to FIG. 9-11, schematic illustrations of embodimentsof communication and processing flow of modules within the contentdistribution network 100 are shown. In some embodiments, thecommunication and processing can be performed in portions of theplatform layer 654 and/or applications layer 656. FIG. 9 depicts a firstembodiment of such communications or processing that can be in theplatform layer 654 and/or applications layer 656 via the message channel412.

The platform layer 654 and/or applications layer 656 can include aplurality of modules that can be embodied in software or hardware. Insome embodiments, some or all of the modules can be embodied in hardwareand/or software at a single location, and in some embodiments, some orall of these modules can be embodied in hardware and/or software atmultiple locations. These modules can perform one or several processesincluding, for example, a presentation process 670, a response process676, a summary model process 680, and a packet selection process 684.

The presentation process 670 can, in some embodiments, include one orseveral method and/or steps to deliver content to one or several userdevices 106 and/or supervisor devices 110. The presentation process 670can be performed by a presenter module 672 and a view module 674. Thepresenter module 672 can be a hardware or software module of the contentdistribution network 100, and specifically of the server 102. In someembodiments, the presenter module 672 can include one or severalportions, features, and/or functionalities that are located on theserver 102 and/or one or several portions, features, and/orfunctionalities that are located on the user device 106. In someembodiments, the presenter module 672 can be embodied in thepresentation system 408.

The presenter module 672 can control the providing of content to one orseveral user devices 106 and/or supervisor devices 110. Specifically,the presenter module 672 can control the generation of one or severalmessages to provide content to one or several desired user devices 106and/or supervisor devices 110. The presenter module 672 can furthercontrol the providing of these one or several messages to the desiredone or several desired user devices 106 and/or supervisor devices 110.Thus, in some embodiments, the presenter module 672 can control one orseveral features of the communications subsystem 532 to generate andsend one or several electrical signals comprising content to one orseveral user devices 106 and/or supervisor devices 110.

In some embodiments, the presenter module 672 can control and/or managea portion of the presentation functions of the presentation process 670,and can specifically manage an “outer loop” of presentation functions.As used herein, the outer loop refers to tasks relating to the trackingof a user's progress through all or a portion of a group of datapackets. In some embodiments, this can include the identification of oneor several completed data packets or nodes and/or the non-adaptiveselection of one or several next data packets or nodes according to, forexample, one or several fixed rules. Such non-adaptive selection doesnot rely on the use of predictive models, but rather on rulesidentifying next data packets based on data relating to the completionof one or several previously completed data packets or assessmentsand/or whether one or several previously completed data packets weresuccessfully completed.

In some embodiments, and due to the management of the outer loop ofpresentation functions including the non-adaptive selection of one orseveral next data packets, nodes, or tasks by the presenter module, thepresenter module can function as a recommendation engine referred toherein as a first recommendation engine or a rules-based recommendationengine. In some embodiments, the first recommendation engine can beconfigured to select a next node for a user based on one or all of theuser's current location in the content network; potential next nodes;the user's history including the user's previous responses; and one orseveral guard conditions associated with the potential next nodes. Insome embodiments, a guard condition defines one or several prerequisitesfor entry into, or exit from, a node.

In some embodiments, the presenter module 672 can include a portionlocated on the server 102 and/or a portion located on the user device106. In some embodiments, the portion of the presenter module 672located on the server 102 can receive data packet information andprovide a subset of the received data packet information to the portionof the presenter module 672 located on the user device 106. In someembodiments, this segregation of functions and/or capabilities canprevent solution data from being located on the user device 106 and frombeing potentially accessible by the user of the user device 106.

In some embodiments, the portion of the presenter module 672 located onthe user device 106 can be further configured to receive the subset ofthe data packet information from the portion of the presenter module 672located on the server 102 and provide that subset of the data packetinformation to the view module 674. In some embodiments, the portion ofthe presenter module 672 located on the user device 106 can be furtherconfigured to receive a content request from the view module 674 and toprovide that content request to the portion of the presenter module 674located on the server 102.

The view module 674 can be a hardware or software module of some or allof the user devices 106 and/or supervisor devices 110 of the contentdistribution network 100. The view module 674 can receive one or severalelectrical signals and/or communications from the presenter module 672and can provide the content received in those one or several electricalsignals and/or communications to the user of the user device 106 and/orsupervisor device 110 via, for example, the I/O subsystem 526.

In some embodiments, the view module 674 can control and/or monitor an“inner loop” of presentation functions. As used herein, the inner looprefers to tasks relating to the tracking and/or management of a user'sprogress through a data packet. This can specifically relate to thetracking and/or management of a user's progression through one orseveral pieces of content, questions, assessments, and/or the like of adata packet. In some embodiments, this can further include the selectionof one or several next pieces of content, next questions, nextassessments, and/or the like of the data packet for presentation and/orproviding to the user of the user device 106.

In some embodiments, one or both of the presenter module 672 and theview module 674 can comprise one or several presentation engines. Insome embodiments, these one or several presentation engines can comprisedifferent capabilities and/or functions. In some embodiments, one of thepresentation engines can be configured to track the progress of a userthrough a single data packet, task, content item, or the like, and insome embodiments, one of the presentation engines can track the progressof a user through a series of data packets, tasks, content items, or thelike.

The response process 676 can comprise one or several methods and/orsteps to evaluate a response. In some embodiments, this can include, forexample, determining whether the response comprises a desired responseand/or an undesired response. In some embodiments, the response process676 can include one or several methods and/or steps to determine thecorrectness and/or incorrectness of one or several received responses.In some embodiments, this can include, for example, determining thecorrectness and/or incorrectness of a multiple choice response, atrue/false response, a short answer response, an essay response, or thelike. In some embodiments, the response processor can employ, forexample, natural language processing, semantic analysis, or the like indetermining the correctness or incorrectness of the received responses.

In some embodiments, the response process 676 can be performed by aresponse processor 678. The response processor 678 can be a hardware orsoftware module of the content distribution network 100, andspecifically of the server 102. In some embodiments, the responseprocessor 678 can be embodied in the response system 406. In someembodiments, the response processor 678 can be communicatively connectedto one or more of the modules of the presentation process 670 such as,for example, the presenter module 672 and/or the view module 674. Insome embodiments, the response processor 678 can be communicativelyconnected with, for example, the message channel 412 and/or othercomponents and/or modules of the content distribution network 100.

The summary model process 680 can comprise one or several methods and/orsteps to generate and/or update one or several models. In someembodiments, this can include, for example, implementing informationreceived either directly or indirectly from the response processor 678to update one or several models. In some embodiments, the summary modelprocess 680 can include the update of a model relating to one or severaluser attributes such as, for example, a user skill model, a userknowledge model, a learning style model, or the like. In someembodiments, the summary model process 680 can include the update of amodel relating to one or several content attributes including attributesrelating to a single content item and/or data packet and/or attributesrelating to a plurality of content items and/or data packets. In someembodiments, these models can relate to an attribute of the one orseveral data packets such as, for example, difficulty, discrimination,required time, or the like.

In some embodiments, the summary model process 680 can be performed bythe model engine 682. In some embodiments, the model engine 682 can be ahardware or software module of the content distribution network 100, andspecifically of the server 102. In some embodiments, the model engine682 can be embodied in the summary model system 404.

In some embodiments, the model engine 682 can be communicativelyconnected to one or more of the modules of the presentation process 760such as, for example, the presenter module 672 and/or the view module674, can be connected to the response processor 678 and/or therecommendation. In some embodiments, the model engine 682 can becommunicatively connected to the message channel 412 and/or othercomponents and/or modules of the content distribution network 100.

The packet selection process 684 can comprise one or several stepsand/or methods to identify and/or select a data packet for presentationto a user. In some embodiments, this data packet can comprise aplurality of data packets. In some embodiments, this data packet can beselected according to one or several models updated as part of thesummary model process 680. In some embodiments, this data packet can beselected according to one or several rules, probabilities, models, orthe like. In some embodiments, the one or several data packets can beselected by the combination of a plurality of models updated in thesummary model process 680 by the model engine 682. In some embodiments,these one or several data packets can be selected by a recommendationengine 686. The recommendation engine 686 can be a hardware or softwaremodule of the content distribution network 100, and specifically of theserver 102. In some embodiments, the recommendation engine 686 can beembodied in the packet selection system 402. In some embodiments, therecommendation engine 686 can be communicatively connected to one ormore of the modules of the presentation process 670, the responseprocess 676, and/or the summary model process 680 either directly and/orindirectly via, for example, the message channel.

In some embodiments, and as depicted in FIG. 9, a presenter module 672can receive a data packet for presentation to a user device 106. Thisdata packet can be received, either directly or indirectly, from arecommendation engine 686. In some embodiments, for example, thepresenter module 672 can receive a data packet for providing to a userdevice 106 from the recommendation engine 686, and in some embodiments,the presenter module 672 can receive an identifier of a data packet forproviding to a user device 106 via a view module 674. This can bereceived from the recommendation engine 686 via a message channel 412.Specifically, in some embodiments, the recommendation engine 686 canprovide data to the message channel 412 indicating the identificationand/or selection of a data packet for providing to a user via a userdevice 106. In some embodiments, this data indicating the identificationand/or selection of the data packet can identify the data packet and/orcan identify the intended recipient of the data packet.

The message channel 412 can output this received data in the form of adata stream 690 which can be received by, for example, the presentermodule 672, the model engine 682, and/or the recommendation engine 686.In some embodiments, some or all of the presenter module 672, the modelengine 682, and/or the recommendation engine 686 can be configured toparse and/or filter the data stream 690 to identify data and/or eventsrelevant to their operation. Thus, for example, the presenter module 672can be configured to parse the data stream for information and/or eventsrelevant to the operation of the presenter module 672.

In some embodiments, the presenter module 672 can, extract the datapacket from the data stream 690 and/or extract data identifying the datapacket and/or indicating the selecting of a data packet from the datastream. In the event that data identifying the data packet is extractedfrom the data stream 690, the presenter module 672 can request andreceive the data packet from the database server 104, and specificallyfrom the content library database 303. In embodiments in which dataindicating the selection of a data packet is extracted from the datastream 690, the presenter module 672 can request and receiveidentification of the data packet from the recommendation engine 686 andthen request and receive the data packet from the database server 104,and specifically from the content library database 303, and in someembodiments in which data indicating the selection of a data packet isextracted from the data stream 690, the presenter module 672 can requestand receive the data packet from the recommendation engine 686.

The presenter module can then, provide the data packet and/or portionsof the data packet to the view module 674. In some embodiments, forexample, the presenter module 672 can retrieve one or several rulesand/or conditions that can be, for example, associated with the datapacket and/or stored in the database server 104. In some embodiments,these rules and/or conditions can identify portions of a data packet forproviding to the view module 674 and/or portions of a data packet to notprovide to the view module 674. In some embodiments, for example,sensitive portions of a data packet, such as, for example, solutioninformation to any questions associated with a data packet, is notprovided to the view module 674 to prevent the possibility of undesiredaccess to those sensitive portions of the data packet. Thus, in someembodiments, the one or several rules and/or conditions can identifyportions of the data packet for providing to the view module 674 and/orportions of the data packet for not providing to the view module.

In some embodiments, the presenter module 672 can, according to the oneor more rules and/or conditions, generate and transmit an electronicmessage containing all or portions of the data packet to the view module674. The view module 674 can receive these all or portions of the datapacket and can provide all or portions of this information to the userof the user device 106 associated with the view module 674 via, forexample, the I/O subsystem 526. In some embodiments, as part of theproviding of all or portions of the data packet to the user of the viewmodule 674, one or several user responses can be received by the viewmodule 674. In some embodiments, these one or several user responses canbe received via the I/O subsystem 526 of the user device 106.

After one or several user responses have been received, the view module674 can provide the one or several user responses to the responseprocessor 678. In some embodiments, these one or several responses canbe directly provided to the response processor 678, and in someembodiments, these one or several responses can be provided indirectlyto the response processor 678 via the message channel 412.

After the response processor 678 receives the one or several responses,the response processor 678 can determine whether the responses aredesired responses and/or the degree to which the received responses aredesired responses. In some embodiments, the response processor can makethis determination via, for example, use of one or several techniques,including, for example, natural language processing (NLP), semanticanalysis, or the like.

In some embodiments, the response processor can determine whether aresponse is a desired response and/or the degree to which a response isa desired response with comparative data which can be associated withthe data packet. In some embodiments, this comparative data cancomprise, for example, an indication of a desired response and/or anindication of one or several undesired responses, a response key, aresponse rubric comprising one or several criterion for determining thedegree to which a response is a desired response, or the like. In someembodiments, the comparative data can be received as a portion of and/orassociated with a data packet. In some embodiments, the comparative datacan be received by the response processor 678 from the presenter module672 and/or from the message channel 412. In some embodiments, theresponse data received from the view module 674 can comprise dataidentifying the user and/or the data packet or portion of the datapacket with which the response is associated. In some embodiments inwhich the response processor 678 merely receives data identifying thedata packet and/or portion of the data packet associated with the one orseveral responses, the response processor 678 can request and/or receivecomparative data from the database server 104, and specifically from thecontent library database 303 of the database server 104.

After the comparative data has been received, the response processor 678determines whether the one or several responses comprise desiredresponses and/or the degree to which the one or several responsescomprise desired responses. The response processor can then provide thedata characterizing whether the one or several responses comprisesdesired responses and/or the degree to which the one or severalresponses comprise desired responses to the message channel 412. Themessage channel can, as discussed above, include the output of theresponse processor 678 in the data stream 690 which can be constantlyoutput by the message channel 412.

In some embodiments, the model engine 682 can subscribe to the datastream 690 of the message channel 412 and can thus receive the datastream 690 of the message channel 412 as indicated in FIG. 9. The modelengine 682 can monitor the data stream 690 to identify data and/orevents relevant to the operation of the model engine. In someembodiments, the model engine 682 can monitor the data stream 690 toidentify data and/or events relevant to the determination of whether aresponse is a desired response and/or the degree to which a response isa desired response.

When a relevant event and/or relevant data is identified by the modelengine, the model engine 682 can take the identified relevant eventand/or relevant data and modify one or several models. In someembodiments, this can include updating and/or modifying one or severalmodels relevant to the user who provided the responses, updating and/ormodifying one or several models relevant to the data packet associatedwith the responses, and/or the like. In some embodiments, these modelscan be retrieved from the database server 104, and in some embodiments,can be retrieved from the model data source 309 of the database server104.

After the models have been updated, the updated models can be stored inthe database server 104. In some embodiments, the model engine 682 cansend data indicative of the event of the completion of the model updateto the message channel 412. The message channel 412 can incorporate thisinformation into the data stream 690 which can be received by therecommendation engine 686. The recommendation engine 686 can monitor thedata stream 690 to identify data and/or events relevant to the operationof the recommendation engine 686. In some embodiments, therecommendation engine 686 can monitor the data stream 690 to identifydata and/or events relevant to the updating of one or several models bythe model engine 682.

When the recommendation engine 686 identifies information in the datastream 690 indicating the completion of the summary model process 680for models relevant to the user providing the response and/or for modelsrelevant to the data packet provided to the user, the recommendationengine 686 can identify and/or select a next data packet for providingto the user and/or to the presentation process 470. In some embodiments,this selection of the next data packet can be performed according to oneor several rules and/or conditions. After the next data packet has beenselected, the recommendation engine 686 can provide information to themodel engine 682 identifying the next selected data packet and/or to themessage channel 412 indicating the event of the selection of the nextcontent item. After the message channel 412 receives informationidentifying the selection of the next content item and/or receives thenext content item, the message channel 412 can include this informationin the data stream 690 and the process discussed with respect to FIG. 9can be repeated.

With reference now to FIG. 10, a schematic illustration of a secondembodiment of communication or processing that can be in the platformlayer 654 and/or applications layer 656 via the message channel 412 isshown. In the embodiment depicted in FIG. 10, the data packet providedto the presenter module 672 and then to the view module 674 does notinclude a prompt for a user response and/or does not result in thereceipt of a user response. As no response is received, when the datapacket is completed, nothing is provided to the response processor 678,but rather data indicating the completion of the data packet is providedfrom one of the view module 674 and/or the presenter module 672 to themessage channel 412. The data is then included in the data stream 690and is received by the model engine 682 which uses the data to updateone or several models. After the model engine 682 has updated the one orseveral models, the model engine 682 provides data indicating thecompletion of the model updates to the message channel 412. The messagechannel 412 then includes the data indicating the completion of themodel updates in the data stream 690 and the recommendation engine 686,which can subscribe to the data stream 690, can extract the dataindicating the completion of the model updates from the data stream 690.The recommendation engine 686 can then identify a next one or severaldata packets for providing to the presenter module 672, and therecommendation engine 686 can then, either directly or indirectly,provide the next one or several data packets to the presenter module672.

With reference now to FIG. 11, a schematic illustration of an embodimentof dual communication, or hybrid communication, in the platform layer654 and/or applications layer 656 is shown. Specifically, in thisembodiment, some communication is synchronous with the completion of oneor several tasks and some communication is asynchronous. Thus, in theembodiment depicted in FIG. 11, the presenter module 672 communicatessynchronously with the model engine 682 via a direct communication 692and communicates asynchronously with the model engine 682 via themessage channel 412.

Specifically, and with reference to FIG. 11, the presenter module 672can receive and/or select a data packet for presentation to the userdevice 106 via the view module 674. In some embodiments, the presentermodule 672 can identify all or portions of the data packet that can beprovided to the view module 674 and portions of the data packet forretaining form the view module 674. In some embodiments, the presentermodule can provide all or portions of the data packet to the view module674. In some embodiments, and in response to the receipt of all orportions of the data packet, the view module 674 can provide aconfirmation of receipt of the all or portions of the data packet andcan provide those all or portions of the data packet to the user via theuser device 106. In some embodiments, the view module 674 can providethose all or portions of the data packet to the user device 106 whilecontrolling the inner loop of the presentation of the data packet to theuser via the user device 106.

After those all or portions of the data packet have been provided to theuser device 106, a response indicative of the completion of one orseveral tasks associated with the data packet can be received by theview module 674 from the user device 106, and specifically from the I/Osubsystem 526 of the user device 106. In response to this receive, theview module 674 can provide an indication of this completion status tothe presenter module 672 and/or can provide the response to the responseprocessor 678.

After the response has been received by the response processor 678, theresponse processor 678 can determine whether the received response is adesired response. In some embodiments, this can include, for example,determining whether the response comprises a correct answer and/or thedegree to which the response comprises a correct answer.

After the response processor has determined whether the receivedresponse is a desired response, the response processor 678 can providean indicator of the result of the determination of whether the receivedresponse is a desired response to the presenter module 672. In responseto the receipt of the indicator of whether the result of thedetermination of whether the received response is a desired response,the presenter module 672 can synchronously communicate with the modelengine 682 via a direct communication 692 and can asynchronouslycommunicate with model engine 682 via the message channel 412. In someembodiments, the synchronous communication can advantageously includetwo-way communication between the model engine 682 and the presentermodule 672 such that the model engine 682 can provide an indication tothe presenter module 672 when model updating is completed by the modelengine.

After the model engine 682 has received one or both of the synchronousand asynchronous communications, the model engine 682 can update one orseveral models relating to, for example, the user, the data packet, orthe like. After the model engine 682 has completed the updating of theone or several models, the model engine 682 can send a communication tothe presenter module 672 indicating the completion of the updated one orseveral modules.

After the presenter module 672 receives the communication indicating thecompletion of the updating of the one or several models, the presentermodule 672 can send a communication to the recommendation engine 686requesting identification of a next data packet. As discussed above, therecommendation engine 686 can then retrieve the updated model andretrieve the user information. With the updated models and the userinformation, the recommendation engine can identify a next data packetfor providing to the user, and can provide the data packet to thepresenter module 672. In some embodiments, the recommendation engine 686can further provide an indication of the next data packet to the modelengine 682, which can use this information relating to the next datapacket to update one or several models, either immediately, or afterreceiving a communication from the presenter module 672 subsequent tothe determination of whether a received response for that data packet isa desired response.

With reference now to FIG. 12, a schematic illustration of oneembodiment of the presentation process 670 is shown. Specifically, FIG.12 depicts multiple portions of the presenter module 672, namely, theexternal portion 673 and the internal portion 675. In some embodiments,the external portion 673 of the presenter module 672 can be located inthe server, and in some embodiments, the internal portion 675 of thepresenter module 672 can be located in the user device 106. In someembodiments, the external portion 673 of the presenter module can beconfigured to communicate and/or exchange data with the internal portion675 of the presenter module 672 as discussed herein. In someembodiments, for example, the external portion 673 of the presentermodule 672 can receive a data packet and can parse the data packet intoportions for providing to the internal portion 675 of the presentermodule 672 and portions for not providing to the internal portion 675 ofthe presenter module 672. In some embodiments, the external portion 673of the presenter module 672 can receive a request for additional dataand/or an additional data packet from the internal portion 675 of thepresenter module 672. In such an embodiment, the external portion 673 ofthe presenter module 672 can identify and retrieve the requested dataand/or the additional data packet from, for example, the database server104 and more specifically from the content library database 104.

With reference now to FIG. 13, a flowchart illustrating one embodimentof a process 440 for data management is shown. In some embodiments, theprocess 440 can be performed by the content management server 102, andmore specifically by the presentation system 408 and/or by thepresentation module or presentation engine. In some embodiments, theprocess 440 can be performed as part of the presentation process 670.

The process 440 begins at block 442, wherein a data packet isidentified. In some embodiments, the data packet can be a data packetfor providing to a student-user. In some embodiments, the data packetcan be identified based on a communication received either directly orindirectly from the recommendation engine 686.

After the data packet has been identified, the process 440 proceeds toblock 444, wherein the data packet is requested. In some embodiments,this can include the requesting of information relating to the datapacket such as the data forming the data packet. In some embodiments,this information can be requested from, for example, the content librarydatabase 303. After the data packet has been requested, the process 440proceeds to block 446, wherein the data packet is received. In someembodiments, the data packet can be received by the presentation system408 from, for example, the content library database 303.

After the data packet has been received, the process 440 proceeds toblock 448, wherein one or several data components are identified. Insome embodiments, for example, the data packet can include one orseveral data components which can, for example, contain different data.In some embodiments, one of these data components, referred to herein asa presentation component, can include content for providing to the user,which content can include one or several requests and/or questionsand/or the like. In some embodiments, one of these data components,referred to herein as a response component, can include data used inevaluating one or several responses received from the user device 106 inresponse to the data packet, and specifically in response to thepresentation component and/or the one or several requests and/orquestions of the presentation component. Thus, in some embodiments, theresponse component of the data packet can be used to ascertain whetherthe user has provided a desired response or an undesired response.

After the data components have been identified, the process 440 proceedsto block 450, wherein a delivery data packet is identified. In someembodiments, the delivery data packet can include the one or severaldata components of the data packets for delivery to a user such as theuser via the user device 106. In some embodiments, the delivery packetcan include the presentation component, and in some embodiments, thedelivery packet can exclude the response packet. After the delivery datapacket has been generated, the process 440 proceeds to block 452,wherein the delivery data packet is provided to the user device 106 andmore specifically to the view module 674. In some embodiments, this caninclude providing the delivery data packet to the user device 106 via,for example, the communication network 120.

After the delivery data packet has been provided to the user device 106,the process 440 proceeds to block 454, wherein the data packet and/orone or several components thereof is sent to and/or provided to theresponse processor 678. In some embodiments, this sending of the datapacket and/or one or several components thereof to the responseprocessor can include receiving a response from the user, and sendingthe response to the user to the response processor simultaneous with thesending of the data packet and/or one or several components thereof tothe response processor. In some embodiments, for example, this caninclude providing the response component to the response processor. Insome embodiments, the response component can be provided to the responseprocessor from the presentation system 408.

With reference now to FIG. 14, a flowchart illustrating one embodimentof a process 460 for evaluating a response is shown. In someembodiments, the process can be performed as a part of the responseprocess 676 and can be performed by, for example, the response system406 and/or by the response processor 678. In some embodiments, theprocess 460 can be performed by the response system 406 in response tothe receipt of a response, either directly or indirectly, from the userdevice 106 or from the view module 674.

The process 460 begins at block 462, wherein a response is receivedfrom, for example, the user device 106 via, for example, thecommunication network 120. After the response has been received, theprocess 460 proceeds to block 464, wherein the data packet associatedwith the response is received. In some embodiments, this can includereceiving all or one or several components of the data packet such as,for example, the response component of the data packet. In someembodiments, the data packet can be received by the response processorfrom the presentation engine.

After the data packet has been received, the process 460 proceeds toblock 466, wherein the response type is identified. In some embodiments,this identification can be performed based on data, such as metadataassociated with the response. In other embodiments, this identificationcan be performed based on data packet information such as the responsecomponent.

In some embodiments, the response type can identify one or severalattributes of the one or several requests and/or questions of the datapacket such as, for example, the request and/or question type. In someembodiments, this can include identifying some or all of the one orseveral requests and/or questions as true/false, multiple choice, shortanswer, essay, or the like.

After the response type has been identified, the process 460 proceeds toblock 468, wherein the data packet and the response are compared todetermine whether the response comprises a desired response and/or anundesired response. In some embodiments, this can include comparing thereceived response and the data packet to determine if the receivedresponse matches all or portions of the response component of the datapacket, to determine the degree to which the received response matchesall or portions of the response component, to determine the degree towhich the received response embodies one or several qualities identifiedin the response component of the data packet, or the like. In someembodiments, this can include classifying the response according to oneor several rules. In some embodiments, these rules can be used toclassify the response as either desired or undesired. In someembodiments, these rules can be used to identify one or several errorsand/or misconceptions evidenced in the response. In some embodiments,this can include, for example: use of natural language processingsoftware and/or algorithms; use of one or several digital thesauruses;use of lemmatization software, dictionaries, and/or algorithms; or thelike.

After the data packet and the response have been compared, the process460 proceeds to block 470 wherein response desirability is determined.In some embodiments this can include, based on the result of thecomparison of the data packet and the response, whether the response isa desired response or is an undesired response. In some embodiments,this can further include quantifying the degree to which the response isa desired response. This determination can include, for example,determining if the response is a correct response, an incorrectresponse, a partially correct response, or the like. In someembodiments, the determination of response desirability can include thegeneration of a value characterizing the response desirability and thestoring of this value in one of the databases 104 such as, for example,the user profile database 301. After the response desirability has beendetermined, the process 460 proceeds to block 472, wherein an assessmentvalue is generated. In some embodiments, the assessment value can be anaggregate value characterizing response desirability for one or more ofa plurality of responses. This assessment value can be stored in one ofthe databases 104 such as the user profile database 301.

In some embodiments, content provisioning performed in accordance withthe processes of FIGS. 11 through 14 can provide significant benefitsover current content provisioning with a computer, especially overcurrent content provisioning with a computer in an educationalenvironment. In some embodiments, content provisioning as described inFIGS. 11 through 14 can be based on real-time and dynamic prioritizationthat can be based on models of one or several user attributes such asuser skill level, models of one or several task attributes, such as taskdifficulty levels, or the like. This provides the significant benefit ofaccurately selecting content most suited for delivery which increasesthe efficiency with which content is provided to the user.

Embodiments of the present disclosure relate to systems and methods forimproving content creation, content curation, input receipt, andadaptivity. Historically, education has been accomplished via direct orindirect interactions between students and one or several teachers.While this educational model can be successful, problems arise when thenumber of students increases with respect to the number of teachers,when students struggle to master content, and/or when a teacher mustselect content for providing to one or several students.

The integration of computers into the educational space has promised tosolve these problems and improve learning and educational outcomes.However, the reality has fallen short of the hoped improvements. Forexample, well a recommendation engine, may be able to select andrecommend content for providing to a student legacy content thatpredates, in many instances, the current digital educational space isunavailable for presentation and is unknown to recommendation engines.Further, because of the volume of this legacy content, the bringing ofthis legacy content into advance educational systems is prohibitivelyexpensive.

In other instances, what content may be provided to a student, receiptof responses from the student is limited in many ways. For example,while a student may interact with the user interface to input one orseveral numbers, letters, characters, such interfaces do not easily lendthemselves for lengthy solution activity as may be required forevaluation of a math problem, or a math-based related problem. Further,well scoring engines may be able to evaluate a response to a problem,scoring engines have been unable to or have struggled in evaluatingsteps to solving a problem. Accordingly, improvements to recommendationengines, content curation engines, scoring engines, and/or othercomponents or modules of a learning system are desired.

The present disclosure includes solutions to these problems. Forinstance, the present disclosure relates to systems and methods forcontent curation and/or content creation. These systems and methods canbe used to bring legacy content into the digital world by, for example,identifying traits or attributes of the legacy content, groupingportions of the legacy content, identifying learning objectives of thelegacy content, or the like. Some embodiments of the present disclosurefurther relate to the training of one or several models for contentcreation and/or content curation. These embodiments, can include systemsand methods whereby training of a machine learning model can beautomated to thereby allow closed-loop unsupervised training.Additionally, some embodiments of the present disclosure relate tosystems and/or methods of content creation, according to one or severalreceived inputs and/or systems and/or methods of content customizationaccording to attributes extracted from one or several user profiles.

The present disclosure relates to systems and methods for receiving userinput at an educational system, such as the content distribution network100. These systems enable, for example, identification of one or severalsteps taken to solve a problem can be presented to the user in the formof a content item. In some embodiments, the end point can be receivedvia, for example, handwriting on a touchscreen, equation editor, OCR,voice, eye movement, handwriting, brainwave interpretation, braincoupling scanning, a biological response, and/or photo. In someembodiments, this can include parsing of a received digital response toidentify one or several steps in solving a problem.

The present disclosure relates to scoring adaptivity, and/or contentrecommendation. This can include the identification of one or severalsteps in response, the evaluation of these one or several steps inresponse, providing remediation based on the evaluation of these one orseveral steps, and/or providing next content based on the evaluation ofthese one or several steps. This can further include the generation ofone or several profiles tracking and/or predicting a user's movementthrough a learning graph, such as a domain graph.

FIGS. 15 through 23. Illustrate embodiments of systems and methods forautomated and direct network positioning and/or automated user interfacecustomization. In some embodiments, automated and direct networkpositioning can include determining a user's position within a contentstructure, also referred to herein as a hierarchy, and/or within acontent network. This can be achieved via the provisioning of aplurality of assessment data packets, each of which can comprise one orseveral questions, the receiving of user response to the providedplurality of assessment data packets, the determining of an initialposition of the user within the hierarchy, and the modification of thatinitial position within the hierarchy based on the received responses.In some embodiments, this placement can be performed via the directlinking of the assessment data packets to the hierarchy such that eachof the assessment data packets is associated with the position in thehierarchy.

The direct linking of the assessment data packets to the hierarchystands in contrast to methods in which each assessment data packet isassociated with data characterizing a difficulty. The assessment datapacket. This difficulty is used to determine a user skill level, whichuser skill level is then translated into a location within a contentnetwork. Methods using difficulty levels present challenges, as comparedto the methods disclosed herein where assessment data packets aredirectly linked to positions with the hierarchy, these challengesincluding requiring additional processing resources, requiringadditional services within a product, and increasing the number ofstatistical models required to determine the user's position within thecontent network. Each of these challenges increases, the processing costof determining the user position in the content network and specificallyincreases processing times, increases set up efforts as additionalmodels need to be created and trained, and decreases accuracy due to thetranslation from difficulty to skill level to position.

With reference now to FIG. 15, a flowchart illustrating one embodimentof a process 700 for automated determining of user position within thehierarchy is shown. The process 700 can be performed by all or portionsof the content distribution network 100 including, for example, theserver 102. In some embodiments, the process 700 can be performed by allor portions of the service is shown in FIGS. 9 through 12.

The process 700 begins a block 702 wherein a data packet is received. Insome embodiments, the received data packet can be an assessment datapacket, which assessment data packet can comprise one or severalquestions for providing to the user. In some embodiments, the datapacket received in block 702 is a new data packet that is not previouslyconnected and/or associated with the hierarchy, and specifically, thedata packet received in block 702, is not yet linked with a position inthe hierarchy. In some embodiments, in some embodiments, the data packetcan be received from, for example, one or several components of thecontent distribution network 100 including, for example, the contentserver 112, the supervisor device 110, or the like.

After the data packet has been received, the process 700 proceeds toblock 700 for wherein data packet metadata is retrieved and/or received.In some embodiments, the data packet metadata can be retrieved and/orreceived from the one or several components of the content distributionnetwork 100 from which the data packet was received. After the datapacket metadata has been retrieved and/or received, the process 700proceeds to block 706, wherein the data packet position, the hierarchyis identified and/or determined. In some embodiments, this position isidentified and/or determined based on information contained in the datapacket metadata, and in some embodiments, this position, the hierarchycan be determined according to analysis of the data packet, and/or ofthe data packet metadata. In some embodiments, for example, a vectorcharacterizing content of the data packet, and/or the data packetmetadata can be generated and can be compared to one or several vectorscharacterizing content of positions within the hierarchy. Based onsimilarity between these vectors, the position of the data packet withinthe hierarchy can be determined. After the data packet position in thehierarchy has been determined, the process 700 proceeds to block 708,wherein the data packet is associated with the identified and/ordetermined position within the hierarchy.

A block 710. User information is received. In some embodiments, the userinformation can include information identifying the user such as, forexample, a username, password, user login, or the like. In someembodiments, this information can further identify one or severalcourses, or glasses in which the user is enrolled and/or for which aplacement assessment is indicated. This information can be received bythe server 102 from the user device 106 via, for example, thecommunication network. After the user information is received, theprocess 700 proceeds to block 712, wherein a relevant hierarchy, and/orpotential data packets are identified. In some embodiments, the relevanthierarchy can be identified based on information received from the user,which information can identify one or several courses, or classes inwhich the user is enrolled and for which a placement assessment isindicated. After the hierarchy is been identified, this step can furtherinclude identifying one or several potential data packets, whichpotential data packets can be data packets associated with thehierarchy, and in some embodiments, can include data packets associatedwith the hierarchy and which have not been previously presented to theuser. The hierarchy and/or the potential data packets can be identifiedby the server 102, and/or by one or several services of the server 102.

After the hierarchy, and/or potential data packets been identified, theprocess 700 proceeds to block 714, wherein an initial user location inthe hierarchy is determined. In some embodiments, this user location thehierarchy can be determined based on information associated with theuser, which information can be retrieved from the database server 104and specifically from the user profile database 301. This retrievedinformation can, in some embodiments, identify previously determinedknowledge or skill levels of user, previously determined location of theuser in a hierarchy, previously completed courses, assignments,assessments, or the like. In some embodiments, the initial user locationin the hierarchy can be a predetermined location, which predeterminedlocation can be applied to users regardless of previous userinteractions with the content distribution network 100. The userlocation the hierarchy can be determined by, for example, the server102.

After the user location the hierarchy has been determined, the process700 proceeds to block 716, wherein one or several assessment datapackets are presented to the user. In some embodiments, these one orseveral assessment data packets that are presented to the user caninclude the data packet received in block 702. In some embodiments, eachof some or all of these data packets can have a difficulty levelrepresented by their position in the hierarchy. In some embodiments,this can include the selection of one or several assessment data packetsby, for example, the packet selection process 684, and the presentationof the packets to the user via the presentation process 670.

At block 718 user responses are received. These responses can beresponses to the presented assessment data packets. In some embodiments,these responses can be received by the presentation process 670, and canbe provided to the response process 676, which can evaluate userresponses indicated in block 720. In some embodiments, the steps ofblock 716, 718, and 720 can be sequentially performed, and in someembodiments, the steps of these blocks can be iteratively performed suchthat the first assessment data packet is presented to the user, a userresponse received, the user response is evaluated, and then, based onthe result of the evaluation of user response, an additional data packetis presented to the user. In some embodiments, steps 714 through 720 canbe iteratively repeated and until one or several termination conditionsor criteria have been met as indicated in decision state 721. If thesetermination criteria are met, then the process 700 proceeds to block722, if these termination criteria are not met, then the process returnsto block 714.

At block 722, the user location within the hierarchy is adjusted basedon the evaluation results of the user responses. In some embodiment,this adjustment can include moving the user location in the hierarchy toa more advanced position or to a less advanced position. In someembodiments, this adjustment within the hierarchy can be made accordingto one or several equations and/or models, which models can comprise oneor several statistical models and/or one or several machine-learningmodels. After the user location in the hierarchy has been adjusted, theprocess 700 proceeds to block 724, wherein one or several content datapackets are selected and presented to the user. In some embodiments, theone or several selected content data packets can include, for example,the data packet received in block 702. In some embodiments, thesecontent data packets can be selected based on the adjusted user locationin the hierarchy. These content data packets can be selected by thepacket selection process 684 and can be presented to the user via thepresentation process 670.

With reference now to FIG. 16, a flowchart illustrating one embodimentof a process 730 for data packet presentation is shown. The process 730can be performed as a part of or in the place of step 716 of, FIG. 15.The process 730 begins at block 732, wherein an instantaneous userlocation in the hierarchy is determined. This instantaneous location isthe location of the user at the time of the determination of block 732,and represents the most updated user location in the hierarchy, takinginto account, in some embodiments, any previously received userresponse. The instantaneous user location can be determined by queryingthe user profile database 301, which can, in some embodiments, includeinformation relating to the user location in the hierarchy.

After the instantaneous user location is determined, the process 716proceeds to block 734, wherein an assessment data packet is selected.This assessment data packet can be selected from the set of potentialassessment data packets that can be, for example, stored in the contentlibrary database 303. In some embodiments, the assessment data packetcan be selected based on the likelihood of the user correctly respondingto the assessment data packet, based on the discrimination of theassessment data packet, or the like. In some embodiments, the datapacket can be selected according shown in FIG. 19, below. The assessmentdata packet can be selected by the packet selection process 684. Afterthe assessment data packet has been selected, the process 730 proceedsto block 736, wherein the selected assessment data packet is presentedto the user. In some embodiments, the selected assessment data packetcan be presented to the user according to the presentation process 670.

With reference now to FIG. 17, a flowchart illustrating one embodimentof a process 740 for evaluating user responses is shown. The process 740can be performed as a part cA or in the place of the step 720 of FIG.15. The process 740 begins a block 742 wherein response creek. This isdetermined. In some embodiments, this can include receiving informationfrom the database server 104 for determining the correctness of thereceived response. This information can be, in some embodiments,retrieved from the content library database 303, and/or from theevaluation database 308. In some embodiments, this information can beused by, for example, the response processor 678, to evaluate thereceived response. In some embodiments, the evaluation of the responsecan include determining whether the received response is correct, and/orthe degree to which the received response is correct.

After the correctness of the responses been determined, the process 740proceeds to block 744 wherein an adjustment to the instantaneous userlocation the hierarchy is determined. This adjustment can be determinedbased on the correctness of the received response. In some embodiments,for example, the adjustment can comprise a movement to relatively moredifficult portion of the hierarchy when the response is correct, oralternatively a movement to a relatively easier portion, the hierarchywhen the response is incorrect. In some embodiments, the adjustment tothe user location within the hierarchy can be made by the responseprocess 676, and/or the summary model process 680.

In some embodiments, this can include determining for a plurality ofpositions within the hierarchy a probability of the user correctlyresponding to assessment data packets of each of those plurality ofpositions within the hierarchy. In some embodiments, this determinationcan be made according to a model such as a logistic model, and morespecifically to a model such as a three parameter logistic model. Insome embodiments, the three parameter logistic model determines aprobability of the user correctly responding to assessment data packetsof a position within the hierarchy according to the user's instantaneoususer location in the hierarchy. In some embodiments, the probability ofa correct response for an item within the hierarchy can be calculatedaccording to the following equation, wherein a_(i) is the itemdiscrimination, b_(i) is the item location, c_(i) is the pseudo-guessingparameter for the item I, and θ is the instantaneous location in thehierarchy. The probability of a correct response for a location in thehierarchy can then be determined by calculating an average from theprobability for correct response for some or all of the items at alocation in the hierarchy.

${P_{i}(\theta)} = {c_{i} + \frac{1 - c_{i}}{1 + {\exp \left\lbrack {a_{i}\left( {\theta - b_{i}} \right)} \right\rbrack}}}$

In some embodiments, b_(i) is defined as an as an integer valuerepresenting a position within the hierarchy. For example, in anembodiment in which the hierarchy represents a table of contents, eachchapter in the table of content could be associated with a uniqueposition in the hierarchy, and in such an embodiment, the value of b_(i)could be the integer value representing the chapter such that chapter 1may be represented by the b_(i) value of “1” and chapter 22 may berepresented by the b_(i) value of “22”. The value of c_(i) is indicativeof the likelihood of the user correctly responding without knowing theanswer. In some embodiments, the c value is set to zero, unless theassessment data packet comprises a multiple choice question, in whichcase, the c value is set equal to (1/(number of answer choices)). Insuch an embodiment in which, for example, the assessment data packetcomprises a multiple choice question with four answer options, the cvalue is set to 0.25. The a value characterizes the degree to which acorrect or incorrect response to the assessment data packetdistinguishes between user skill levels. Normally, the a value is setbetween 0.2 and 2.0, and is generally set to 1.0.

In some embodiments, the user's instantaneous user location in thehierarchy is determined according to a estimation method, whichestimation method can, in some embodiments, comprise an expected aposteriori (EAP) estimation method. One embodiment of an EAP estimationmethod is shown in the equations below, in which U_(q) is one of Mquadrature points on the latent scale, W(U_(q)) is the point density atquadrature point U_(q) given the prior distribution, and L(θ) is thelikelihood of the item responses at quadrature point U_(q).

${\hat{\theta}}_{EAP} = \frac{\sum\limits_{q = 1}^{M}{U_{q}{W\left( U_{q} \right)}{L(\theta)}}}{\sum\limits_{q = 1}^{M}{{W\left( U_{q} \right)}{L(\theta)}}}$${S.E._{EAP}} = \left\lbrack \frac{\sum\limits_{q = 1}^{M}{\left( {U_{q} - {\hat{\theta}}_{EAP}} \right)^{2}{W\left( U_{q} \right)}{L(\theta)}}}{\sum\limits_{q = 1}^{M}{{W\left( U_{q} \right)}{L(\theta)}}} \right\rbrack^{1/2}$

In some embodiments, L(θ) by the follow equation, in which Xi is theobserved response for item i (Xi=0 for an incorrect response & Xi=1 fora correct response), I is the number of items administered, and Q(θ) isequal to 1−P(θ).

${L(\theta)} = {\prod\limits_{i = 1}^{I}\; {{P_{i}(\theta)}^{X_{i}}{Q_{i}(\theta)}^{1 - X_{i}}}}$

Estimation of the user's instantaneous location according the thisexemplary embodiment disclosed herein utilized information relating tothe user's previous location. In some embodiments in which previouslocation information does not exist, prior beliefs about a user locationcan be incorporated into the estimation method. In one exemplaryembodiment, a uniform distribution can be used, which uniformdistribution makes placement in each of the locations in the hierarchyequally probable.

After the update to the instantaneous location has been determined, theprocess 740 proceeds to block 746 wherein the instantaneous location isupdated, and specifically, wherein the adjusted location is updated andstored in the database server 104 and specifically in the user profiledatabase 301. In some embodiments, this can include the creating of anew instantaneous location via the application of the determinedadjustment to the instantaneous location. This update to theinstantaneous location can be made by the server 102, and in someembodiments, can be made by the response process 676, and/or the summarymodel process 680.

With reference now to FIG. 18, a flowchart illustrating one embodimentof a process 750 for determining the adjustment to the instantaneoususer location is shown. The process 750 can, in some embodiments, beperformed in the place of, or as a part of step 722 of FIG. 15. Theprocess 750 begins a block 752 wherein a probability of a correctresponse to assessment data packets from a position within the hierarchyis determined. In some embodiments, such a probability can be calculatedfor each of the positions within the hierarchy. This probability can becalculated according to the equations and steps outlined in paragraphs[00239]-[00243], above.

After the probability of correct response is determined for hierarchypositions, the process 750 proceeds to block 754, wherein the determinedprobabilities are compared to a threshold value. In some embodiments,this threshold value can be a mastery threshold, which can delineatebetween probabilities indicative of mastery and probabilitiesinsufficient to indicate mastery. The mastery threshold can be retrievedfrom the database server 104, and specifically from the evaluationdatabase 308. In some embodiments, the probabilities determined in block752 can be compared to the threshold in block 754, after whichcomparison, the process 750 can proceed to block 756, wherein the lowestposition in the hierarchy failing to have a probability indicative ofmastery, or in other words, failing to meet or exceed the masterythreshold is identified. In some embodiments, this location can beidentified as the updated user location in the hierarchy.

With reference now to FIG. 19, a flowchart illustrating one embodimentof a process 760 for selecting an assessment data packet is shown. Theprocess 760 can be performed, in some embodiments, as a part of or inthe place of the step of block 734 of FIG. 16. The process 760 begins atblock 762, wherein available data packets are identified. In someembodiments, an available data packet is a data packet such as, forexample, an assessment data packet that is associated with thehierarchy, and/or that has not been previously provided to the user. Insome embodiments, available data packets can be identified fromassessment data packets contained in the database server 104 andspecifically in the content library database 303.

After available data packets have been identified, the process 760proceeds to block 764, wherein an information criterion for each of thedata packets and/or for each of the potential data packets iscalculated. This information criterion can, in some embodiments,characterize the amount of information carried by the potential datapackets. In some embodiments this information criterion can compriseFisher information that can, in some embodiments characterize the amountof information that the data packet carries, which information relatesto, in some embodiments, user mastery of content associated withlocations in the hierarchy, and/or to user location the hierarchy.

At block 766, an average information criterion is calculated for eachposition in the hierarchy. In such an embodiment, a position in thehierarchy is identified, potential data packets associated with thatposition are identified, information criterion for those potential datapackets is retrieved, and an average value of the information criterionof those data packets is calculated. This can be repeated until averageinformation criterion is calculated for some or all of the positions inthe hierarchy. This calculation can be performed by the server 102.

After average information criterion has been calculated, the process 760proceeds to block 768 wherein the position in the hierarchy with thehighest average information criterion is identified. In someembodiments, this can include a comparison of average informationcriterion calculated for each of the positions in the hierarchy, andidentification of the position having the highest average informationcriterion. After the position with the highest average informationcriterion has been identified, the process 760 proceeds to block 770,wherein a data packet from the identified position in the hierarchy isselected. In some embodiments, this includes randomly selecting a datapacket from the identified position in the hierarchy, and/or selectingthe data packet having the highest information criterion from theposition in the hierarchy.

With reference now to FIG. 20, a schematic illustration of oneembodiment of an exemplary user interface (UX) 800 is shown. The userinterface 800 can include a plurality of Windows and/or panels throughwhich content can be presented to the user, responses can be receivedfrom the user, and/or functionalities can be provided. These windows caninclude, for example, a presentation window 802 wherein content can bepresented to the user, a response window 804 wherein one or several userresponses can be entered, and/or facilitator menu 806. In someembodiments, the facilitator menu can include active features which can,when manipulated, provide the user with one or several masteryfacilitators. These mastery facilitators can include one or severallearning aids such as, for example, a demonstration of how to solve orrespond to the assessment data packet, step-by-step instructions forsolving and/or for responding to the assessment data packet, one orseveral tips or hints for solving and/or responding to the assessmentdata packet, or the like.

With reference now to FIG. 21, a flowchart illustrating one embodimentof a process 840 for automated user interface customization is shown.The process 840 can be performed by all or portions the contentdistribution network 100 including for example, the server 102. Theprocess 840 can be performed using for example, the user interface 800shown in FIG. 20. The process 840 begins a block 842 wherein a useridentifier is received. This user identifier can comprise a username,password, user login information, a unique user identifier, or the like.After the user information identifier has been received, the process 840proceeds to block 844 wherein the user is associated with the useridentifier is identified and the user metadata of the identified user isretrieved. In some embodiments, the user can be identified based on thereceived user identifier, and information contained in user profiledatabase, and likewise user metadata can be retrieved from the userprofile database 301.

After the user has been identified and/or user metadata has beenretrieved, the process 840 proceeds to block 846 wherein the userinterface 800 is launched and/or wherein the server 102 directs thelaunch of the user interface 800 on the user device 106, and/or on thesupervisor device 110. In some embodiments, the user interface cancomprise a plurality of windows. In some embodiments, content can bepresented to the user in at least one of the plurality of windows, and auser response can be received in at least one of the plurality ofwindows. After the user interface is then launched, the process 840proceeds to block 848, wherein an assessment data packet is presented.In some embodiments, the presenting of the assessment data packet caninclude the selecting of an assessment data packet for presenting by,for example, the packet selection process 684, and/or the presenting ofthe packet via the presentation process 670. In some embodiments, and asa part of the presenting of the step of block 848, a response to thepresented assessment data packet can be received by, for example, thepresentation process 670, and can be evaluated by the response process676.

At block 850, a readiness value is generated. The readiness value cancharacterize a user's progress through content and/or mastery of thecontent, and specifically can indicate a user's readiness to receive amastery assessment. In some embodiments, the readiness value can begenerated based on responses received from the user to the assessmentdata packet presented in block 848. In some embodiments, the readinessvalue can be calculated by the server 102, and specifically by theresponse process 676.

At block 852 one or several deactivation thresholds can be retrieved.The deactivation thresholds can comprises one or several values, each ofwhich can be associated with a readiness value and functionality fordeactivation when the deactivation threshold is met or exceeded. In someembodiments, the functionality for deactivation associated with thedeactivation threshold can be, for example, a mastery facilitator and/ora content presentation function whereby one or several content datapackets are provided to the user. In some embodiments, for example, asthe user's readiness increases, one or several mastery facilitators areprogressively deactivated, such that a user with a relatively higherreadiness level may have access to relatively fewer mastery facilitatorsthan a user with a relatively lower mastery level. In some embodiments,each of the deactivation thresholds is associated with a readiness valueand functionality for deactivation when the deactivation threshold ismet or exceeded.

After the deactivation thresholds have been retrieved, the process 840proceeds to block 854, wherein one or several user interfacefunctionalities are deactivated when the deactivation thresholds are metor exceeded. This deactivation of one or several functionalities caninclude modifying the user interface. In some embodiments, this caninclude determining which, if any, deactivation thresholds are met orexceeded, and deactivating functionalities associated with thosedeactivation thresholds. In some embodiments, for example, adeactivation threshold may be met or exceeded, which deactivationthreshold is associated with deactivation of one or several contentpresentation functionalities of the user interface 800, and/or thedeactivation threshold is associated with deactivation of one or severalof the mastery facilitators of the user interface 800. In embodiments inwhich a functionality is deactivated, the user interface 800 may changeaccording to the deactivation to remove indication of the deactivatedfunctionality from the user interface 800, to change the appearance ofthe deactivated functionality such as by, for example, greying-out thedeactivated functionality, or the like.

In some embodiments, functionalities can be deactivated in apredetermined order such as, for example, from most helpful to leasthelpful. Thus, in one embodiment, a demonstration of how to solve orrespond to the assessment data packet may be deactivated when a first,lowest deactivation threshold is met, a step-by-step instructions forsolving and/or for responding to the assessment data packet may bedeactivated when a second, intermediate deactivation threshold is met,and, one or several tips or hints for solving and/or responding to theassessment data packet can be deactivated when a third, highestdeactivation threshold is met. In such an embodiment, a relatively lowerdeactivation threshold indicates a relatively lower level of readiness.Thus, in such an embodiment, as the user's readiness increases, one orseveral mastery facilitators are progressively deactivated, such that auser with a relatively higher readiness level may have access torelatively fewer mastery facilitators than a user with a relativelylower mastery level. The functionalities of the user interface 800 canbe deactivated by the server 102 and/or the device 106, 110 displayingthe user interface 800.

At block 856 of the process 840, a mastery threshold is retrieved. Themastery threshold can comprise a value delineating between readinessvalues indicating readiness to receive a mastery assessment andreadiness values indicative of non-readiness to receive the masteryassessment. The mastery threshold can, in some embodiments, be retrievedfrom the database server 104, and specifically from the thresholddatabase 310. At block 858, the readiness score generated in block 850can be compared to the retrieved mastery threshold. This comparison canbe performed by the server 102 and/or the device 106, 110 displaying theuser interface 800.

At decision state 860 of process 840, it is determined whether toterminate the presenting of assessment data packets to the user. In someembodiments, this determination can be made according to a comparison ofone or several attributes relevant to the presentation of assessmentdata packets to one or several termination criteria. In someembodiments, these criteria can include, for example, a terminationcriteria associated with a sufficiently high readiness score, asufficiently low readiness score, a maximum number of presentedassessment data packets, a maximum length of time of presentation ofassessment data packets, a maximum number of correct or incorrectresponses, or the like. If it is determined not to terminated thepresentation of assessment data packets, then the process 840 returns toblock 848 and proceeds as discussed above. In such an embodiment, thisloop continue until one or several termination criteria are met resultsin the iterative selection and presentation of content, and specificallyassessment data packets to the user until at least one of thetermination criteria is met.

If at least one of the termination criteria is met, or in someembodiments, if the readiness value exceeds the mastery threshold, thenthe process 840 proceeds to block 862, wherein the mastery assessment isprovided. In some embodiments, this mastery assessment can determinemastery of one or several objectives associated with the user locationin the hierarchy. This determination of mastery can be achieved with thepresentation of one or several assessment data packets to the user,which assessment data packets can include an associated difficulty levelwhich can be used to determine a user skill level and mastery. In someembodiments, the mastery assessment can stand in contrast to the processof FIG. 15 in that mastery can be determined based on difficulty levelsseparate from the hierarchy and associated with assessment data packetspresented as part of the mastery assessment.

With reference now to FIG. 22, a flowchart illustrating one embodimentof a process 870 for generating a readiness score is shown. The process870 can be performed as a part of or in the place of step 850 of FIG.21. The process 870 begins at block 872, wherein a response set isidentified. In some embodiments, the response set identifies some numberof responses for use in generating the readiness value. In someembodiments, the responses in the response set are received from theuser and relate to presented assessment data packets. In someembodiments, the set of responses can comprise a number of the mostrecent response, and specifically can comprise the four most recentlyreceived responses.

After the response set has been identified, the process 870 proceeds toblock 874, wherein mastery facilitator usage is determined for responsesin the response set. In some embodiments, this can include identifyinglearning aids used in connection with the response in the response set,and specifically can include determining the number of learning aidsused and/or the type of used learning aids. In some embodiments, themastery facilitator usage can be determined based on informationassociated with the received responses, which information can be storedin the database server 104, and specifically in the user profiledatabase 301. The usage of mastery facilitators can be characterized viageneration of a vector indicative of the determined usage. After themastery facilitator usage is determined, the process 870 proceeds toblock 876, wherein the readiness value is generated. In someembodiments, the readiness value can generated as a function of thecorrectness of responses in the set of responses reduced by a valuecharacterizing mastery facilitator usage.

A number of variations and modifications of the disclosed embodimentscan also be used. Specific details are given in the above description toprovide a thorough understanding of the embodiments. However, it isunderstood that the embodiments may be practiced without these specificdetails. For example, well-known circuits, processes, algorithms,structures, and techniques may be shown without unnecessary detail inorder to avoid obscuring the embodiments.

Implementation of the techniques, blocks, steps and means describedabove may be done in various ways. For example, these techniques,blocks, steps and means may be implemented in hardware, software, or acombination thereof. For a hardware implementation, the processing unitsmay be implemented within one or more application specific integratedcircuits (ASICs), digital signal processors (DSPs), digital signalprocessing devices (DSPDs), programmable logic devices (PLDs), fieldprogrammable gate arrays (FPGAs), processors, controllers,micro-controllers, microprocessors, other electronic units designed toperform the functions described above, and/or a combination thereof.

Also, it is noted that the embodiments may be described as a processwhich is depicted as a flowchart, a flow diagram, a swim diagram, a dataflow diagram, a structure diagram, or a block diagram. Although adepiction may describe the operations as a sequential process, many ofthe operations can be performed in parallel or concurrently. Inaddition, the order of the operations may be re-arranged. A process isterminated when its operations are completed, but could have additionalsteps not included in the figure. A process may correspond to a method,a function, a procedure, a subroutine, a subprogram, etc. When a processcorresponds to a function, its termination corresponds to a return ofthe function to the calling function or the main function.

Furthermore, embodiments may be implemented by hardware, software,scripting languages, firmware, middleware, microcode, hardwaredescription languages, and/or any combination thereof. When implementedin software, firmware, middleware, scripting language, and/or microcode,the program code or code segments to perform the necessary tasks may bestored in a machine readable medium such as a storage medium. A codesegment or machine-executable instruction may represent a procedure, afunction, a subprogram, a program, a routine, a subroutine, a module, asoftware package, a script, a class, or any combination of instructions,data structures, and/or program statements. A code segment may becoupled to another code segment or a hardware circuit by passing and/orreceiving information, data, arguments, parameters, and/or memorycontents. Information, arguments, parameters, data, etc. may be passed,forwarded, or transmitted via any suitable means including memorysharing, message passing, token passing, network transmission, etc.

For a firmware and/or software implementation, the methodologies may beimplemented with modules (e.g., procedures, functions, and so on) thatperform the functions described herein. Any machine-readable mediumtangibly embodying instructions may be used in implementing themethodologies described herein. For example, software codes may bestored in a memory. Memory may be implemented within the processor orexternal to the processor. As used herein the term “memory” refers toany type of long team, short team, volatile, nonvolatile, or otherstorage medium and is not to be limited to any particular type of memoryor number of memories, or type of media upon which memory is stored.

Moreover, as disclosed herein, the term “storage medium” may representone or more memories for storing data, including read only memory (ROM),random access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine readable mediums for storing information. The term“machine-readable medium” includes, but is not limited to portable orfixed storage devices, optical storage devices, and/or various otherstorage mediums capable of storing that contain or carry instruction(s)and/or data.

While the principles of the disclosure have been described above inconnection with specific apparatuses and methods, it is to be clearlyunderstood that this description is made only by way of example and notas limitation on the scope of the disclosure.

What is claimed is:
 1. A system for automated and direct networkpositioning, the system comprising: memory comprising a content librarydatabase comprising: a plurality of assessment data packets and aplurality of content data packets; and a structure sub-databaseidentifying a hierarchy with which each of the plurality of assessmentdata packets and each of the content data packets are associated,wherein the hierarchy comprises a plurality of positions; at least oneserver configured to: receive a data packet previously unassociated withthe hierarchy in the structure sub-database; identify one of theplurality of positions within the hierarchy for the data packet; receiveuser information; present a series of assessment data packets to theuser; receive a response from the user subsequent to presentation ofeach of the assessment data packets; evaluate the received responses;adjust a location of the user within the hierarchy based on theevaluating of the received responses; and present a content data packetto the user, wherein the content data packet is selected for presentingto the user based on the adjusted location of the user within thehierarchy.
 2. The system of claim 1, wherein the series of assessmentdata packets comprises the received data packet.
 3. The system of claim1, wherein the presented content data packet comprises the received datapacket.
 4. The system of claim 1, wherein the location of the userwithin the hierarchy is adjusted according to a machine-learning model.5. The system of claim 4, wherein each of the assessment data packets isunassociated with an independent difficulty level.
 6. The system ofclaim 1, wherein the difficulty level of each of the assessment datapackets is represented by the position of each of the assessment datapackets in the hierarchy.
 7. The system of claim 1, wherein presenting aseries of assessment data packets comprises: determining aninstantaneous user location in the hierarchy; selecting an assessmentdata packet based on the instantaneous user location in the hierarchy;and presenting the selected assessment data packet to the user.
 8. Thesystem of claim 7, evaluating user responses comprises: determinecorrectness of received response; determining an adjustment to theinstantaneous user location in the hierarchy based on the correctness ofthe received response; and updating the instantaneous user location inthe hierarchy according to the adjustment.
 9. The system of claim 8,wherein determining an adjustment to the instantaneous user location inthe hierarchy comprises: determining for a plurality of positions withinthe hierarchy a probability of the user correctly responding toassessment data packets of each of those plurality of positions withinthe hierarchy according to a logistic model; comparing the probabilitiesof the user correctly responding to the assessment data packets with amastery threshold; and identifying the lowest position within thehierarchy for which the probability of the user correctly responding tothe assessment data packets of that position fails to meet or exceed themastery threshold.
 10. The system of claim 9, wherein the logistic modelcomprises a three parameter logistic model.
 11. The system of claim 10,wherein the three parameter logistic model determines a probability ofthe user correctly responding to assessment data packets of a positionwithin the hierarchy according to the instantaneous user location in thehierarchy.
 12. The system of claim 11, wherein the instantaneous userlocation in the hierarchy is determined according to a estimationmethod.
 13. The system of claim 12, wherein the estimation methodcomprises an expected a posteriori (EAP) estimation method.
 14. Thesystem of claim 12, wherein the assessment data packet is selected inpart based on information criterion values calculated for assessmentdata packets available for selecting, wherein one of the assessment datapackets is available for selecting when the one of the assessment datapackets is not previously presented to the user.
 15. The system of claim14, wherein selecting the assessment data packet based on informationcriterion values comprises: identifying assessment data packetsavailable for selecting; calculating an information criterion value foreach of the of the assessment data packets available for selecting;calculating an average information criterion value for each position inthe hierarchy from the information criterion values of the assessmentdata packets associated with each position in the hierarchy; identifyingthe position in the hierarchy having the highest average informationcriterion value; and selecting one of the assessment data packetsassociated with the position in the hierarchy identified as having thehighest average information criterion value.
 16. The system of claim 15,wherein the one of the assessment data packets is randomly selected fromthe assessment data packets of the position in the hierarchy identifiedas having the highest average information criterion value.
 17. A methodfor automated and direct network positioning, the method comprising:receiving user information from a user; presenting a series ofassessment data packets to the user; receiving a response from the usersubsequent to presentation of each of the assessment data packets;evaluating the received responses; adjusting a location of the userwithin a hierarchy based on the evaluating of the received responses;and presenting a content data packet to the user, wherein the contentdata packet is selected for presenting to the user based on the adjustedlocation of the user within the hierarchy.
 18. The method of claim 17,further comprising: receiving a data packet previously unassociated withthe hierarchy; and identifying one of a plurality of positions withinthe hierarchy for the data packet; and associating the received datapacket with the one of the positions within the hierarchy.
 19. Themethod of claim 18, wherein the series of assessment data packetscomprises the received data packet, and wherein the presented contentdata packet comprises the received data packet.
 20. The method of claim19, wherein presenting a series of assessment data packets comprises:determining an instantaneous user location in the hierarchy; selectingan assessment data packet based on the instantaneous user location inthe hierarchy; and presenting the selected assessment data packet to theuser; and wherein evaluating user responses comprises: evaluating thereceived response; determining an adjustment to the instantaneous userlocation in the hierarchy; and updating the instantaneous user locationin the hierarchy according to the adjustment.